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Treatment Trends and Outcomes in Healthcare-Associated Pneumonia
Bacterial pneumonia remains an important cause of morbidity and mortality in the United States, and is the 8th leading cause of death with 55,227 deaths among adults annually.1 In 2005, the American Thoracic Society (ATS) and the Infectious Diseases Society of America (IDSA) collaborated to update guidelines for hospital-acquired pneumonia (HAP), ventilator-associated pneumonia, and healthcare-associated pneumonia (HCAP).2 This broad document outlines an evidence-based approach to diagnostic testing and antibiotic management based on the epidemiology and risk factors for these conditions. The guideline specifies the following criteria for HCAP: hospitalization in the past 90 days, residence in a skilled nursing facility (SNF), home infusion therapy, hemodialysis, home wound care, family members with multidrug resistant organisms (MDRO), and immunosuppressive diseases or medications, with the presumption that these patients are more likely to be harboring MDRO and should thus be treated empirically with broad-spectrum antibiotic therapy. Prior studies have shown that patients with HCAP have a more severe illness, are more likely to have MDRO, are more likely to be inadequately treated, and are at a higher risk for mortality than patients with community-acquired pneumonia (CAP).3,4
These guidelines are controversial, especially in regard to the recommendations to empirically treat broadly with 2 antibiotics targeting Pseudomonas species, whether patients with HCAP merit broader spectrum coverage than patients with CAP, and whether the criteria for defining HCAP are adequate to predict which patients are harboring MDRO. It has subsequently been proposed that HCAP is more related to CAP than to HAP, and a recent update to the guideline removed recommendations for treatment of HCAP and will be placing HCAP into the guidelines for CAP instead.5 We sought to investigate the degree of uptake of the ATS and IDSA guideline recommendations by physicians over time, and whether this led to a change in outcomes among patients who met the criteria for HCAP.
METHODS
Setting and Patients
We identified patients discharged between July 1, 2007, and November 30, 2011, from 488 US hospitals that participated in the Premier database (Premier Inc., Charlotte, North Carolina), an inpatient database developed for measuring quality and healthcare utilization. The database is frequently used for healthcare research and has been described previously.6 Member hospitals are in all regions of the US and are generally reflective of US hospitals. This database contains multiple data elements, including sociodemographic information, International Classification of Diseases, 9th Revision-Clinical Modification (ICD-9-CM) diagnosis and procedure codes, hospital and physician information, source of admission, and discharge status. It also includes a date-stamped log of all billed items and services, including diagnostic tests, medications, and other treatments. Because the data do not contain identifiable information, the institutional review board at our medical center determined that this study did not constitute human subjects research.
We included all patients aged ≥18 years with a principal diagnosis of pneumonia or with a secondary diagnosis of pneumonia paired with a principal diagnosis of respiratory failure, acute respiratory distress syndrome, respiratory arrest, sepsis, or influenza. Patients were excluded if they were transferred to or from another acute care institution, had a length of stay of 1 day or less, had cystic fibrosis, did not have a chest radiograph, or did not receive antibiotics within 48 hours of admission.
For each patient, we extracted age, gender, principal diagnosis, comorbidities, and the specialty of the attending physician. Comorbidities were identified from ICD-9-CM secondary diagnosis codes and Diagnosis Related Groups by using Healthcare Cost and Utilization Project Comorbidity Software, version 3.1, based on the work of Elixhauser (Agency for Healthcare Research and Quality, Rockville, Maryland).7 In order to ensure that patients had HCAP, we required the presence of ≥1 HCAP criteria, including hospitalization in the past 90 days, hemodialysis, admission from an SNF, or immune suppression (which was derived from either a secondary diagnosis for neutropenia, hematological malignancy, organ transplant, acquired immunodeficiency virus, or receiving immunosuppressant drugs or corticosteroids [equivalent to ≥20 mg/day of prednisone]).
Definitions of Guideline-Concordant and Discordant Antibiotic Therapy
The ATS and IDSA guidelines recommended the following antibiotic combinations for HCAP: an antipseudomonal cephalosporin or carbapenem or a beta-lactam/lactamase inhibitor, plus an antipseudomonal quinolone or aminoglycoside, plus an antibiotic with activity versus methicillin resistant Staphylococcus aureus (MRSA), such as vancomycin or linezolid. Based on these guidelines, we defined the receipt of fully guideline-concordant antibiotics as 2 recommended antibiotics for Pseudomonas species plus 1 for MRSA administered by the second day of admission. Partially guideline-concordant antibiotics were defined as 1 recommended antibiotic for Pseudomonas species plus 1 for MRSA by the second day of hospitalization. Guideline-discordant antibiotics were defined as all other combinations.
Statistical Analysis
Descriptive statistics on patient characteristics are presented as frequency, proportions for categorical factors, and median with interquartile range (IQR) for continuous variables for the full cohort and by treatment group, defined as fully or partially guideline-concordant antibiotic therapy or discordant therapy. Hospital rates of fully guideline-concordant treatment are presented overall and by hospital characteristics. The association of hospital characteristics with rates of fully guideline-concordant therapy were assessed by using 1-way analysis of variance tests.
To assess trends across hospitals for the association between the use of guideline-concordant therapy and mortality, progression to respiratory failure as measured by the late initiation of invasive mechanical ventilation (day 3 or later), and the length of stay among survivors, we divided the 4.5-year study period into 9 intervals of 6 months each; 292 hospitals that submitted data for all 9 time points were examined in this analysis. Based on the distribution of length of stay in the first time period, we created an indicator variable for extended length of stay with length of stay at or above the 75th percentile, defined as extended. For each hospital at each 6-month interval, we then computed risk-standardized guideline-concordant treatment (RS-treatment) rates and risk-standardized in-hospital outcome rates similar to methods used by the Centers for Medicare and Medicaid Services for public reporting.8 For each hospital at each time interval, we estimated a predicted rate of guideline-concordant treatment as the sum of predicted probabilities of guideline-concordant treatment from patient factors and the random intercept for the hospital in which they were admitted. We then calculated the expected rate of guideline-concordant treatment as the sum of expected probabilities of treatment received from patient factors only. RS-treatment was then calculated as the ratio of predicted to expected rates multiplied by the overall unadjusted mean treatment rate from all patients.9 We repeated the same modeling strategy to calculate risk-standardized outcome (RS-outcome) rates for each hospital across all time points. All models were adjusted for patient demographics and comorbidities. Similar models using administrative data have moderate discrimination for mortality.10
We then fit mixed-effects linear models with random hospital intercept and slope across time for the RS-treatment and outcome rates, respectively. From these models, we estimated the mean slope for RS-treatment and for RS-outcome over time. In addition, we estimated a slope or trend over time for each hospital for treatment and for outcome and evaluated the correlation between the treatment and outcome trends.
All analyses were performed using the Statistical Analysis System version 9.4 (SAS Institute Inc., Cary, NC) and STATA release 13 (StataCorp, LLC, College Station, Texas).
RESULTS
DISCUSSION
In this large, retrospective cohort study, we found that there was a substantial gap between the empiric antibiotics recommended by the ATS and IDSA guidelines and the empiric antibiotics that patients actually received. Over the study period, we saw an increased adherence to guidelines, in spite of growing evidence that HCAP risk factors do not adequately predict which patients are at risk for infection with an MDRO.11 We used this change in antibiotic prescribing behavior over time to determine if there was a clinical impact on patient outcomes and found that at the hospital level, there were no improvements in mortality, excess length of stay, or progression to respiratory failure despite a doubling in guideline-concordant antibiotic use.
At least 2 other large studies have assessed the association between guideline-concordant therapy and outcomes in HCAP.12,13 Both found that guideline-concordant therapy was associated with increased mortality, despite propensity matching. Both were conducted at the individual patient level by using administrative data, and results were likely affected by unmeasured clinical confounders, with sicker patients being more likely to receive guideline-concordant therapy. Our focus on the outcomes at the hospital level avoids this selection bias because the overall severity of illness of patients at any given hospital would not be expected to change over the study period, while physician uptake of antibiotic prescribing guidelines would be expected to increase over time. Determining the correlation between increases in guideline adherence and changes in patient outcome may offer a better assessment of the impact of guideline adherence. In this regard, our results are similar to those achieved by 1 quality improvement collaborative that was aimed at increasing guideline concordant therapy in ICUs. Despite an increase in guideline concordance from 33% to 47% of patients, they found no change in overall mortality.14
There were several limitations to our study. We did not have access to microbiologic data, so we were unable to determine which patients had MDRO infection or determine antibiotic-pathogen matching. However, the treating physicians in our study population presumably did not have access to this data at the time of treatment either because the time period we examined was within the first 48 hours of hospitalization, the interval during which cultures are incubating and the patients are being treated empirically. In addition, there may have been HCAP patients that we failed to identify, such as patients who were admitted in the past 90 days to a hospital that does not submit data to Premier. However, it is unlikely that prescribing for such patients should differ systematically from what we observed. While the database draws from 488 hospitals nationwide, it is possible that practices may be different at facilities that are not contained within the Premier database, such as Veterans Administration Hospitals. Similarly, we did not have readings for chest x-rays; hence, there could be some patients in the dataset who did not have pneumonia. However, we tried to overcome this by including only those patients with a principal diagnosis of pneumonia or sepsis with a secondary pneumonia diagnosis, a chest x-ray, and antibiotics administered within the first 48 hours of admission.
There are likely several reasons why so few HCAP patients in our study received guideline-concordant antibiotics. A lack of knowledge about the ATS and IDSA guidelines may have impacted the physicians in our study population. El-Solh et al.15 surveyed physicians about the ATS-IDSA guidelines 4 years after publication and found that only 45% were familiar with the document. We found that the rate of prescribing at least partially guideline-concordant antibiotics rose steadily over time, supporting the idea that the newness of the guidelines was 1 barrier. Additionally, prior studies have shown that many physicians may not agree with or choose to follow guidelines, with only 20% of physicians indicating that guidelines have a major impact on their clinical decision making,16 and the majority do not choose HCAP guideline-concordant antibiotics when tested.17 Alternatively, clinicians may not follow the guidelines because of a belief that the HCAP criteria do not adequately indicate patients who are at risk for MDRO. Previous studies have demonstrated the relative inability of HCAP risk factors to predict patients who harbor MDRO18 and suggest that better tools such as clinical scoring systems, which include not only the traditional HCAP risk factors but also prior exposure to antibiotics, prior culture data, and a cumulative assessment of both intrinsic and extrinsic factors, could more accurately predict MDRO and lead to a more judicious use of broad-spectrum antimicrobial agents.19-25 Indeed, these collective findings have led the authors of the recently updated guidelines to remove HCAP as a clinical entity from the hospital-acquired or ventilator-associated pneumonia guidelines and place them instead in the upcoming updated guidelines on the management of CAP.5 Of these 3 explanations, the lack of familiarity fits best with our observation that guideline-concordant therapy increased steadily over time with no evidence of reaching a plateau. Ironically, as consensus was building that HCAP is a poor marker for MDROs, routine empiric treatment with vancomycin and piperacillin-tazobactam (“vanco and zosyn”) have become routine in many hospitals. Additional studies are needed to know if this trend has stabilized or reversed.
CONCLUSIONS
In conclusion, clinicians in our large, nationally representative sample treated the majority of HCAP patients as though they had CAP. Although there was an increase in the administration of guideline-concordant therapy over time, this increase was not associated with improved outcomes. This study supports the growing consensus that HCAP criteria do not accurately predict which patients benefit from broad-spectrum antibiotics for pneumonia, and most patients fare well with antibiotics targeting common community-acquired organisms.
Disclosure
This work was supported by grant # R01HS018723 from the Agency for Healthcare Research and Quality. Dr. Lagu is also supported by the National Heart, Lung, and Blood Institute of the National Institutes of Health under award number K01HL114745. Dr. Lindenauer is supported by grant K24HL132008 from the National Heart, Lung, and Blood Institute. The funding agency had no role in the data acquisition, analysis, or manuscript preparation for this study. Drs. Haessler and Rothberg had full access to all the data in the study and take responsibility for the integrity of the data and the accuracy of the data analysis. Drs. Haessler, Lagu, Lindenauer, Skiest, Zilberberg, Higgins, and Rothberg conceived of the study and analyzed and interpreted the data. Dr. Lindenauer acquired the data. Dr. Pekow and Ms. Priya carried out the statistical analyses. Dr. Haessler drafted the manuscript. All authors critically reviewed the manuscript for accuracy and integrity. All authors certify no potential conflicts of interest. Preliminary results from this study were presented in oral and poster format at IDWeek in 2012 and 2013.
1. Kochanek KD, Murphy SL, Xu JQ, Tejada-Vera B. Deaths: Final data for 2014. National vital statistics reports; vol 65 no 4. Hyattsville, MD: National Center for Health Statistics. 2016. PubMed
2. American Thoracic Society, Infectious Diseases Society of America. Guidelines for the Management of Adults with Hospital-acquired, Ventilator-associated, and Healthcare-associated Pneumonia. Am J Respir Crit Care Med. 2005;171(4):388-416. PubMed
3. Zilberberg MD, Shorr A. Healthcare-associated pneumonia: the state of the evidence to date. Curr Opin Pulm Med. 2011;17(3):142-147. PubMed
4. Kollef MK, Shorr A, Tabak YP, Gupta V, Liu LZ, Johannes RS. Epidemiology and Outcomes of Health-care-associated pneumonia. Chest. 2005;128(6):3854-3862. PubMed
5. Kalil AC, Metersky ML, Klompas M, et al. Management of Adults With Hospital-acquired and Ventilator-associated Pneumonia: 2016 Clinical Practice Guidelines by the Infectious Diseases Society of America and the American Thoracic Society. Clin Infect Dis. 2016;63(5):575-582. PubMed
6. Lindenauer PK, Pekow PS, Lahti MC, Lee Y, Benjamin EM, Rothberg MB. Association of corticosteroid dose and route of administration with risk of treatment failure in acute exacerbation of chronic obstructive pulmonary disease. JAMA. 2010;303(23):2359-2367. PubMed
7. Elixhauser A, Steiner C, Harris DR, Coffey RM. Comorbidity measures for use with administrative data. Med Care. 1998;36(1):8-27. PubMed
8. Centers for Medicare & Medicaid Services. Frequently asked questions (FAQs): Implementation and maintenance of CMS mortality measures for AMI & HF. 2007. https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/HospitalQualityInits/downloads/HospitalMortalityAboutAMI_HF.pdf. Accessed November 1, 2016.
9. Normand SL, Shahian DM. Statistical and Clinical Aspects of Hospital Outcomes Profiling. Stat Sci. 2007;22(2):206-226.
10. Rothberg MB, Pekow PS, Priya A, et al. Using highly detailed administrative data to predict pneumonia mortality. PLoS One. 2014;9(1):e87382. PubMed
11. Jones BE, Jones MM, Huttner B, et al. Trends in antibiotic use and nosocomial pathogens in hospitalized veterans with pneumonia at 128 medical centers, 2006-2010. Clin Infect Dis. 2015;61(9):1403-1410. PubMed
12. Attridge RT, Frei CR, Restrepo MI, et al. Guideline-concordant therapy and outcomes in healthcare-associated pneumonia. Eur Respir J. 2011;38(4):878-887. PubMed
13. Rothberg MB, Zilberberg MD, Pekow PS, et al. Association of Guideline-based Antimicrobial Therapy and Outcomes in Healthcare-Associated Pneumonia. J Antimicrob Chemother. 2015;70(5):1573-1579. PubMed
14. Kett DH, Cano E, Quartin AA, et al. Improving Medicine through Pathway Assessment of Critical Therapy of Hospital-Acquired Pneumonia (IMPACT-HAP) Investigators. Implementation of guidelines for management of possible multidrug-resistant pneumonia in intensive care: an observational, multicentre cohort study. Lancet Infect Dis. 2011;11(3):181-189. PubMed
15. El-Solh AA, Alhajhusain A, Saliba RG, Drinka P. Physicians’ Attitudes Toward Guidelines for the Treatment of Hospitalized Nursing-Home -Acquired Pneumonia. J Am Med Dir Assoc. 2011;12(4):270-276. PubMed
16. Tunis S, Hayward R, Wilson M, et al. Internists’ Attitudes about Clinical Practice Guidelines. Ann Intern Med. 1994;120(11):956-963. PubMed
17. Seymann GB, Di Francesco L, Sharpe B, et al. The HCAP Gap: Differences between Self-Reported Practice Patterns and Published Guidelines for Health Care-Associated Pneumonia. Clin Infect Dis. 2009;49(12):1868-1874. PubMed
18. Chalmers JD, Rother C, Salih W, Ewig S. Healthcare associated pneumonia does not accurately identify potentially resistant pathogens: a systematic review and meta-analysis. Clin Infect Dis. 2014;58(3):330-339. PubMed
19. Shorr A, Zilberberg MD, Reichley R, et al. Validation of a Clinical Score for Assessing the Risk of Resistant Pathogens in Patients with Pneumonia Presenting to the Emergency Department. Clin Infect Dis. 2012;54(2):193-198. PubMed
20. Aliberti S, Pasquale MD, Zanaboni AM, et al. Stratifying Risk Factors for Multidrug-Resistant Pathogens in Hospitalized Patients Coming from the Community with Pneumonia. Clin Infect Dis. 2012;54(4):470-478. PubMed
21. Schreiber MP, Chan CM, Shorr AF. Resistant Pathogens in Nonnosocomial Pneumonia and Respiratory Failure: Is it Time to Refine the Definition of Health-care-Associated Pneumonia? Chest. 2010;137(6):1283-1288. PubMed
22. Madaras-Kelly KJ, Remington RE, Fan VS, Sloan KL. Predicting antibiotic resistance to community-acquired pneumonia antibiotics in culture-positive patients with healthcare-associated pneumonia. J Hosp Med. 2012;7(3):195-202. PubMed
23. Shindo Y, Ito R, Kobayashi D, et al. Risk factors for drug-resistant pathogens in community-acquired and healthcare-associated pneumonia. Am J Respir Crit Care Med. 2013;188(8):985-995. PubMed
24. Metersky ML, Frei CR, Mortensen EM. Predictors of Pseudomonas and methicillin-resistant Staphylococcus aureus in hospitalized patients with healthcare-associated pneumonia. Respirology. 2016;21(1):157-163. PubMed
25. Webb BJ, Dascomb K, Stenehjem E, Dean N. Predicting risk of drug-resistant organisms in pneumonia: moving beyond the HCAP model. Respir Med. 2015;109(1):1-10. PubMed
Bacterial pneumonia remains an important cause of morbidity and mortality in the United States, and is the 8th leading cause of death with 55,227 deaths among adults annually.1 In 2005, the American Thoracic Society (ATS) and the Infectious Diseases Society of America (IDSA) collaborated to update guidelines for hospital-acquired pneumonia (HAP), ventilator-associated pneumonia, and healthcare-associated pneumonia (HCAP).2 This broad document outlines an evidence-based approach to diagnostic testing and antibiotic management based on the epidemiology and risk factors for these conditions. The guideline specifies the following criteria for HCAP: hospitalization in the past 90 days, residence in a skilled nursing facility (SNF), home infusion therapy, hemodialysis, home wound care, family members with multidrug resistant organisms (MDRO), and immunosuppressive diseases or medications, with the presumption that these patients are more likely to be harboring MDRO and should thus be treated empirically with broad-spectrum antibiotic therapy. Prior studies have shown that patients with HCAP have a more severe illness, are more likely to have MDRO, are more likely to be inadequately treated, and are at a higher risk for mortality than patients with community-acquired pneumonia (CAP).3,4
These guidelines are controversial, especially in regard to the recommendations to empirically treat broadly with 2 antibiotics targeting Pseudomonas species, whether patients with HCAP merit broader spectrum coverage than patients with CAP, and whether the criteria for defining HCAP are adequate to predict which patients are harboring MDRO. It has subsequently been proposed that HCAP is more related to CAP than to HAP, and a recent update to the guideline removed recommendations for treatment of HCAP and will be placing HCAP into the guidelines for CAP instead.5 We sought to investigate the degree of uptake of the ATS and IDSA guideline recommendations by physicians over time, and whether this led to a change in outcomes among patients who met the criteria for HCAP.
METHODS
Setting and Patients
We identified patients discharged between July 1, 2007, and November 30, 2011, from 488 US hospitals that participated in the Premier database (Premier Inc., Charlotte, North Carolina), an inpatient database developed for measuring quality and healthcare utilization. The database is frequently used for healthcare research and has been described previously.6 Member hospitals are in all regions of the US and are generally reflective of US hospitals. This database contains multiple data elements, including sociodemographic information, International Classification of Diseases, 9th Revision-Clinical Modification (ICD-9-CM) diagnosis and procedure codes, hospital and physician information, source of admission, and discharge status. It also includes a date-stamped log of all billed items and services, including diagnostic tests, medications, and other treatments. Because the data do not contain identifiable information, the institutional review board at our medical center determined that this study did not constitute human subjects research.
We included all patients aged ≥18 years with a principal diagnosis of pneumonia or with a secondary diagnosis of pneumonia paired with a principal diagnosis of respiratory failure, acute respiratory distress syndrome, respiratory arrest, sepsis, or influenza. Patients were excluded if they were transferred to or from another acute care institution, had a length of stay of 1 day or less, had cystic fibrosis, did not have a chest radiograph, or did not receive antibiotics within 48 hours of admission.
For each patient, we extracted age, gender, principal diagnosis, comorbidities, and the specialty of the attending physician. Comorbidities were identified from ICD-9-CM secondary diagnosis codes and Diagnosis Related Groups by using Healthcare Cost and Utilization Project Comorbidity Software, version 3.1, based on the work of Elixhauser (Agency for Healthcare Research and Quality, Rockville, Maryland).7 In order to ensure that patients had HCAP, we required the presence of ≥1 HCAP criteria, including hospitalization in the past 90 days, hemodialysis, admission from an SNF, or immune suppression (which was derived from either a secondary diagnosis for neutropenia, hematological malignancy, organ transplant, acquired immunodeficiency virus, or receiving immunosuppressant drugs or corticosteroids [equivalent to ≥20 mg/day of prednisone]).
Definitions of Guideline-Concordant and Discordant Antibiotic Therapy
The ATS and IDSA guidelines recommended the following antibiotic combinations for HCAP: an antipseudomonal cephalosporin or carbapenem or a beta-lactam/lactamase inhibitor, plus an antipseudomonal quinolone or aminoglycoside, plus an antibiotic with activity versus methicillin resistant Staphylococcus aureus (MRSA), such as vancomycin or linezolid. Based on these guidelines, we defined the receipt of fully guideline-concordant antibiotics as 2 recommended antibiotics for Pseudomonas species plus 1 for MRSA administered by the second day of admission. Partially guideline-concordant antibiotics were defined as 1 recommended antibiotic for Pseudomonas species plus 1 for MRSA by the second day of hospitalization. Guideline-discordant antibiotics were defined as all other combinations.
Statistical Analysis
Descriptive statistics on patient characteristics are presented as frequency, proportions for categorical factors, and median with interquartile range (IQR) for continuous variables for the full cohort and by treatment group, defined as fully or partially guideline-concordant antibiotic therapy or discordant therapy. Hospital rates of fully guideline-concordant treatment are presented overall and by hospital characteristics. The association of hospital characteristics with rates of fully guideline-concordant therapy were assessed by using 1-way analysis of variance tests.
To assess trends across hospitals for the association between the use of guideline-concordant therapy and mortality, progression to respiratory failure as measured by the late initiation of invasive mechanical ventilation (day 3 or later), and the length of stay among survivors, we divided the 4.5-year study period into 9 intervals of 6 months each; 292 hospitals that submitted data for all 9 time points were examined in this analysis. Based on the distribution of length of stay in the first time period, we created an indicator variable for extended length of stay with length of stay at or above the 75th percentile, defined as extended. For each hospital at each 6-month interval, we then computed risk-standardized guideline-concordant treatment (RS-treatment) rates and risk-standardized in-hospital outcome rates similar to methods used by the Centers for Medicare and Medicaid Services for public reporting.8 For each hospital at each time interval, we estimated a predicted rate of guideline-concordant treatment as the sum of predicted probabilities of guideline-concordant treatment from patient factors and the random intercept for the hospital in which they were admitted. We then calculated the expected rate of guideline-concordant treatment as the sum of expected probabilities of treatment received from patient factors only. RS-treatment was then calculated as the ratio of predicted to expected rates multiplied by the overall unadjusted mean treatment rate from all patients.9 We repeated the same modeling strategy to calculate risk-standardized outcome (RS-outcome) rates for each hospital across all time points. All models were adjusted for patient demographics and comorbidities. Similar models using administrative data have moderate discrimination for mortality.10
We then fit mixed-effects linear models with random hospital intercept and slope across time for the RS-treatment and outcome rates, respectively. From these models, we estimated the mean slope for RS-treatment and for RS-outcome over time. In addition, we estimated a slope or trend over time for each hospital for treatment and for outcome and evaluated the correlation between the treatment and outcome trends.
All analyses were performed using the Statistical Analysis System version 9.4 (SAS Institute Inc., Cary, NC) and STATA release 13 (StataCorp, LLC, College Station, Texas).
RESULTS
DISCUSSION
In this large, retrospective cohort study, we found that there was a substantial gap between the empiric antibiotics recommended by the ATS and IDSA guidelines and the empiric antibiotics that patients actually received. Over the study period, we saw an increased adherence to guidelines, in spite of growing evidence that HCAP risk factors do not adequately predict which patients are at risk for infection with an MDRO.11 We used this change in antibiotic prescribing behavior over time to determine if there was a clinical impact on patient outcomes and found that at the hospital level, there were no improvements in mortality, excess length of stay, or progression to respiratory failure despite a doubling in guideline-concordant antibiotic use.
At least 2 other large studies have assessed the association between guideline-concordant therapy and outcomes in HCAP.12,13 Both found that guideline-concordant therapy was associated with increased mortality, despite propensity matching. Both were conducted at the individual patient level by using administrative data, and results were likely affected by unmeasured clinical confounders, with sicker patients being more likely to receive guideline-concordant therapy. Our focus on the outcomes at the hospital level avoids this selection bias because the overall severity of illness of patients at any given hospital would not be expected to change over the study period, while physician uptake of antibiotic prescribing guidelines would be expected to increase over time. Determining the correlation between increases in guideline adherence and changes in patient outcome may offer a better assessment of the impact of guideline adherence. In this regard, our results are similar to those achieved by 1 quality improvement collaborative that was aimed at increasing guideline concordant therapy in ICUs. Despite an increase in guideline concordance from 33% to 47% of patients, they found no change in overall mortality.14
There were several limitations to our study. We did not have access to microbiologic data, so we were unable to determine which patients had MDRO infection or determine antibiotic-pathogen matching. However, the treating physicians in our study population presumably did not have access to this data at the time of treatment either because the time period we examined was within the first 48 hours of hospitalization, the interval during which cultures are incubating and the patients are being treated empirically. In addition, there may have been HCAP patients that we failed to identify, such as patients who were admitted in the past 90 days to a hospital that does not submit data to Premier. However, it is unlikely that prescribing for such patients should differ systematically from what we observed. While the database draws from 488 hospitals nationwide, it is possible that practices may be different at facilities that are not contained within the Premier database, such as Veterans Administration Hospitals. Similarly, we did not have readings for chest x-rays; hence, there could be some patients in the dataset who did not have pneumonia. However, we tried to overcome this by including only those patients with a principal diagnosis of pneumonia or sepsis with a secondary pneumonia diagnosis, a chest x-ray, and antibiotics administered within the first 48 hours of admission.
There are likely several reasons why so few HCAP patients in our study received guideline-concordant antibiotics. A lack of knowledge about the ATS and IDSA guidelines may have impacted the physicians in our study population. El-Solh et al.15 surveyed physicians about the ATS-IDSA guidelines 4 years after publication and found that only 45% were familiar with the document. We found that the rate of prescribing at least partially guideline-concordant antibiotics rose steadily over time, supporting the idea that the newness of the guidelines was 1 barrier. Additionally, prior studies have shown that many physicians may not agree with or choose to follow guidelines, with only 20% of physicians indicating that guidelines have a major impact on their clinical decision making,16 and the majority do not choose HCAP guideline-concordant antibiotics when tested.17 Alternatively, clinicians may not follow the guidelines because of a belief that the HCAP criteria do not adequately indicate patients who are at risk for MDRO. Previous studies have demonstrated the relative inability of HCAP risk factors to predict patients who harbor MDRO18 and suggest that better tools such as clinical scoring systems, which include not only the traditional HCAP risk factors but also prior exposure to antibiotics, prior culture data, and a cumulative assessment of both intrinsic and extrinsic factors, could more accurately predict MDRO and lead to a more judicious use of broad-spectrum antimicrobial agents.19-25 Indeed, these collective findings have led the authors of the recently updated guidelines to remove HCAP as a clinical entity from the hospital-acquired or ventilator-associated pneumonia guidelines and place them instead in the upcoming updated guidelines on the management of CAP.5 Of these 3 explanations, the lack of familiarity fits best with our observation that guideline-concordant therapy increased steadily over time with no evidence of reaching a plateau. Ironically, as consensus was building that HCAP is a poor marker for MDROs, routine empiric treatment with vancomycin and piperacillin-tazobactam (“vanco and zosyn”) have become routine in many hospitals. Additional studies are needed to know if this trend has stabilized or reversed.
CONCLUSIONS
In conclusion, clinicians in our large, nationally representative sample treated the majority of HCAP patients as though they had CAP. Although there was an increase in the administration of guideline-concordant therapy over time, this increase was not associated with improved outcomes. This study supports the growing consensus that HCAP criteria do not accurately predict which patients benefit from broad-spectrum antibiotics for pneumonia, and most patients fare well with antibiotics targeting common community-acquired organisms.
Disclosure
This work was supported by grant # R01HS018723 from the Agency for Healthcare Research and Quality. Dr. Lagu is also supported by the National Heart, Lung, and Blood Institute of the National Institutes of Health under award number K01HL114745. Dr. Lindenauer is supported by grant K24HL132008 from the National Heart, Lung, and Blood Institute. The funding agency had no role in the data acquisition, analysis, or manuscript preparation for this study. Drs. Haessler and Rothberg had full access to all the data in the study and take responsibility for the integrity of the data and the accuracy of the data analysis. Drs. Haessler, Lagu, Lindenauer, Skiest, Zilberberg, Higgins, and Rothberg conceived of the study and analyzed and interpreted the data. Dr. Lindenauer acquired the data. Dr. Pekow and Ms. Priya carried out the statistical analyses. Dr. Haessler drafted the manuscript. All authors critically reviewed the manuscript for accuracy and integrity. All authors certify no potential conflicts of interest. Preliminary results from this study were presented in oral and poster format at IDWeek in 2012 and 2013.
Bacterial pneumonia remains an important cause of morbidity and mortality in the United States, and is the 8th leading cause of death with 55,227 deaths among adults annually.1 In 2005, the American Thoracic Society (ATS) and the Infectious Diseases Society of America (IDSA) collaborated to update guidelines for hospital-acquired pneumonia (HAP), ventilator-associated pneumonia, and healthcare-associated pneumonia (HCAP).2 This broad document outlines an evidence-based approach to diagnostic testing and antibiotic management based on the epidemiology and risk factors for these conditions. The guideline specifies the following criteria for HCAP: hospitalization in the past 90 days, residence in a skilled nursing facility (SNF), home infusion therapy, hemodialysis, home wound care, family members with multidrug resistant organisms (MDRO), and immunosuppressive diseases or medications, with the presumption that these patients are more likely to be harboring MDRO and should thus be treated empirically with broad-spectrum antibiotic therapy. Prior studies have shown that patients with HCAP have a more severe illness, are more likely to have MDRO, are more likely to be inadequately treated, and are at a higher risk for mortality than patients with community-acquired pneumonia (CAP).3,4
These guidelines are controversial, especially in regard to the recommendations to empirically treat broadly with 2 antibiotics targeting Pseudomonas species, whether patients with HCAP merit broader spectrum coverage than patients with CAP, and whether the criteria for defining HCAP are adequate to predict which patients are harboring MDRO. It has subsequently been proposed that HCAP is more related to CAP than to HAP, and a recent update to the guideline removed recommendations for treatment of HCAP and will be placing HCAP into the guidelines for CAP instead.5 We sought to investigate the degree of uptake of the ATS and IDSA guideline recommendations by physicians over time, and whether this led to a change in outcomes among patients who met the criteria for HCAP.
METHODS
Setting and Patients
We identified patients discharged between July 1, 2007, and November 30, 2011, from 488 US hospitals that participated in the Premier database (Premier Inc., Charlotte, North Carolina), an inpatient database developed for measuring quality and healthcare utilization. The database is frequently used for healthcare research and has been described previously.6 Member hospitals are in all regions of the US and are generally reflective of US hospitals. This database contains multiple data elements, including sociodemographic information, International Classification of Diseases, 9th Revision-Clinical Modification (ICD-9-CM) diagnosis and procedure codes, hospital and physician information, source of admission, and discharge status. It also includes a date-stamped log of all billed items and services, including diagnostic tests, medications, and other treatments. Because the data do not contain identifiable information, the institutional review board at our medical center determined that this study did not constitute human subjects research.
We included all patients aged ≥18 years with a principal diagnosis of pneumonia or with a secondary diagnosis of pneumonia paired with a principal diagnosis of respiratory failure, acute respiratory distress syndrome, respiratory arrest, sepsis, or influenza. Patients were excluded if they were transferred to or from another acute care institution, had a length of stay of 1 day or less, had cystic fibrosis, did not have a chest radiograph, or did not receive antibiotics within 48 hours of admission.
For each patient, we extracted age, gender, principal diagnosis, comorbidities, and the specialty of the attending physician. Comorbidities were identified from ICD-9-CM secondary diagnosis codes and Diagnosis Related Groups by using Healthcare Cost and Utilization Project Comorbidity Software, version 3.1, based on the work of Elixhauser (Agency for Healthcare Research and Quality, Rockville, Maryland).7 In order to ensure that patients had HCAP, we required the presence of ≥1 HCAP criteria, including hospitalization in the past 90 days, hemodialysis, admission from an SNF, or immune suppression (which was derived from either a secondary diagnosis for neutropenia, hematological malignancy, organ transplant, acquired immunodeficiency virus, or receiving immunosuppressant drugs or corticosteroids [equivalent to ≥20 mg/day of prednisone]).
Definitions of Guideline-Concordant and Discordant Antibiotic Therapy
The ATS and IDSA guidelines recommended the following antibiotic combinations for HCAP: an antipseudomonal cephalosporin or carbapenem or a beta-lactam/lactamase inhibitor, plus an antipseudomonal quinolone or aminoglycoside, plus an antibiotic with activity versus methicillin resistant Staphylococcus aureus (MRSA), such as vancomycin or linezolid. Based on these guidelines, we defined the receipt of fully guideline-concordant antibiotics as 2 recommended antibiotics for Pseudomonas species plus 1 for MRSA administered by the second day of admission. Partially guideline-concordant antibiotics were defined as 1 recommended antibiotic for Pseudomonas species plus 1 for MRSA by the second day of hospitalization. Guideline-discordant antibiotics were defined as all other combinations.
Statistical Analysis
Descriptive statistics on patient characteristics are presented as frequency, proportions for categorical factors, and median with interquartile range (IQR) for continuous variables for the full cohort and by treatment group, defined as fully or partially guideline-concordant antibiotic therapy or discordant therapy. Hospital rates of fully guideline-concordant treatment are presented overall and by hospital characteristics. The association of hospital characteristics with rates of fully guideline-concordant therapy were assessed by using 1-way analysis of variance tests.
To assess trends across hospitals for the association between the use of guideline-concordant therapy and mortality, progression to respiratory failure as measured by the late initiation of invasive mechanical ventilation (day 3 or later), and the length of stay among survivors, we divided the 4.5-year study period into 9 intervals of 6 months each; 292 hospitals that submitted data for all 9 time points were examined in this analysis. Based on the distribution of length of stay in the first time period, we created an indicator variable for extended length of stay with length of stay at or above the 75th percentile, defined as extended. For each hospital at each 6-month interval, we then computed risk-standardized guideline-concordant treatment (RS-treatment) rates and risk-standardized in-hospital outcome rates similar to methods used by the Centers for Medicare and Medicaid Services for public reporting.8 For each hospital at each time interval, we estimated a predicted rate of guideline-concordant treatment as the sum of predicted probabilities of guideline-concordant treatment from patient factors and the random intercept for the hospital in which they were admitted. We then calculated the expected rate of guideline-concordant treatment as the sum of expected probabilities of treatment received from patient factors only. RS-treatment was then calculated as the ratio of predicted to expected rates multiplied by the overall unadjusted mean treatment rate from all patients.9 We repeated the same modeling strategy to calculate risk-standardized outcome (RS-outcome) rates for each hospital across all time points. All models were adjusted for patient demographics and comorbidities. Similar models using administrative data have moderate discrimination for mortality.10
We then fit mixed-effects linear models with random hospital intercept and slope across time for the RS-treatment and outcome rates, respectively. From these models, we estimated the mean slope for RS-treatment and for RS-outcome over time. In addition, we estimated a slope or trend over time for each hospital for treatment and for outcome and evaluated the correlation between the treatment and outcome trends.
All analyses were performed using the Statistical Analysis System version 9.4 (SAS Institute Inc., Cary, NC) and STATA release 13 (StataCorp, LLC, College Station, Texas).
RESULTS
DISCUSSION
In this large, retrospective cohort study, we found that there was a substantial gap between the empiric antibiotics recommended by the ATS and IDSA guidelines and the empiric antibiotics that patients actually received. Over the study period, we saw an increased adherence to guidelines, in spite of growing evidence that HCAP risk factors do not adequately predict which patients are at risk for infection with an MDRO.11 We used this change in antibiotic prescribing behavior over time to determine if there was a clinical impact on patient outcomes and found that at the hospital level, there were no improvements in mortality, excess length of stay, or progression to respiratory failure despite a doubling in guideline-concordant antibiotic use.
At least 2 other large studies have assessed the association between guideline-concordant therapy and outcomes in HCAP.12,13 Both found that guideline-concordant therapy was associated with increased mortality, despite propensity matching. Both were conducted at the individual patient level by using administrative data, and results were likely affected by unmeasured clinical confounders, with sicker patients being more likely to receive guideline-concordant therapy. Our focus on the outcomes at the hospital level avoids this selection bias because the overall severity of illness of patients at any given hospital would not be expected to change over the study period, while physician uptake of antibiotic prescribing guidelines would be expected to increase over time. Determining the correlation between increases in guideline adherence and changes in patient outcome may offer a better assessment of the impact of guideline adherence. In this regard, our results are similar to those achieved by 1 quality improvement collaborative that was aimed at increasing guideline concordant therapy in ICUs. Despite an increase in guideline concordance from 33% to 47% of patients, they found no change in overall mortality.14
There were several limitations to our study. We did not have access to microbiologic data, so we were unable to determine which patients had MDRO infection or determine antibiotic-pathogen matching. However, the treating physicians in our study population presumably did not have access to this data at the time of treatment either because the time period we examined was within the first 48 hours of hospitalization, the interval during which cultures are incubating and the patients are being treated empirically. In addition, there may have been HCAP patients that we failed to identify, such as patients who were admitted in the past 90 days to a hospital that does not submit data to Premier. However, it is unlikely that prescribing for such patients should differ systematically from what we observed. While the database draws from 488 hospitals nationwide, it is possible that practices may be different at facilities that are not contained within the Premier database, such as Veterans Administration Hospitals. Similarly, we did not have readings for chest x-rays; hence, there could be some patients in the dataset who did not have pneumonia. However, we tried to overcome this by including only those patients with a principal diagnosis of pneumonia or sepsis with a secondary pneumonia diagnosis, a chest x-ray, and antibiotics administered within the first 48 hours of admission.
There are likely several reasons why so few HCAP patients in our study received guideline-concordant antibiotics. A lack of knowledge about the ATS and IDSA guidelines may have impacted the physicians in our study population. El-Solh et al.15 surveyed physicians about the ATS-IDSA guidelines 4 years after publication and found that only 45% were familiar with the document. We found that the rate of prescribing at least partially guideline-concordant antibiotics rose steadily over time, supporting the idea that the newness of the guidelines was 1 barrier. Additionally, prior studies have shown that many physicians may not agree with or choose to follow guidelines, with only 20% of physicians indicating that guidelines have a major impact on their clinical decision making,16 and the majority do not choose HCAP guideline-concordant antibiotics when tested.17 Alternatively, clinicians may not follow the guidelines because of a belief that the HCAP criteria do not adequately indicate patients who are at risk for MDRO. Previous studies have demonstrated the relative inability of HCAP risk factors to predict patients who harbor MDRO18 and suggest that better tools such as clinical scoring systems, which include not only the traditional HCAP risk factors but also prior exposure to antibiotics, prior culture data, and a cumulative assessment of both intrinsic and extrinsic factors, could more accurately predict MDRO and lead to a more judicious use of broad-spectrum antimicrobial agents.19-25 Indeed, these collective findings have led the authors of the recently updated guidelines to remove HCAP as a clinical entity from the hospital-acquired or ventilator-associated pneumonia guidelines and place them instead in the upcoming updated guidelines on the management of CAP.5 Of these 3 explanations, the lack of familiarity fits best with our observation that guideline-concordant therapy increased steadily over time with no evidence of reaching a plateau. Ironically, as consensus was building that HCAP is a poor marker for MDROs, routine empiric treatment with vancomycin and piperacillin-tazobactam (“vanco and zosyn”) have become routine in many hospitals. Additional studies are needed to know if this trend has stabilized or reversed.
CONCLUSIONS
In conclusion, clinicians in our large, nationally representative sample treated the majority of HCAP patients as though they had CAP. Although there was an increase in the administration of guideline-concordant therapy over time, this increase was not associated with improved outcomes. This study supports the growing consensus that HCAP criteria do not accurately predict which patients benefit from broad-spectrum antibiotics for pneumonia, and most patients fare well with antibiotics targeting common community-acquired organisms.
Disclosure
This work was supported by grant # R01HS018723 from the Agency for Healthcare Research and Quality. Dr. Lagu is also supported by the National Heart, Lung, and Blood Institute of the National Institutes of Health under award number K01HL114745. Dr. Lindenauer is supported by grant K24HL132008 from the National Heart, Lung, and Blood Institute. The funding agency had no role in the data acquisition, analysis, or manuscript preparation for this study. Drs. Haessler and Rothberg had full access to all the data in the study and take responsibility for the integrity of the data and the accuracy of the data analysis. Drs. Haessler, Lagu, Lindenauer, Skiest, Zilberberg, Higgins, and Rothberg conceived of the study and analyzed and interpreted the data. Dr. Lindenauer acquired the data. Dr. Pekow and Ms. Priya carried out the statistical analyses. Dr. Haessler drafted the manuscript. All authors critically reviewed the manuscript for accuracy and integrity. All authors certify no potential conflicts of interest. Preliminary results from this study were presented in oral and poster format at IDWeek in 2012 and 2013.
1. Kochanek KD, Murphy SL, Xu JQ, Tejada-Vera B. Deaths: Final data for 2014. National vital statistics reports; vol 65 no 4. Hyattsville, MD: National Center for Health Statistics. 2016. PubMed
2. American Thoracic Society, Infectious Diseases Society of America. Guidelines for the Management of Adults with Hospital-acquired, Ventilator-associated, and Healthcare-associated Pneumonia. Am J Respir Crit Care Med. 2005;171(4):388-416. PubMed
3. Zilberberg MD, Shorr A. Healthcare-associated pneumonia: the state of the evidence to date. Curr Opin Pulm Med. 2011;17(3):142-147. PubMed
4. Kollef MK, Shorr A, Tabak YP, Gupta V, Liu LZ, Johannes RS. Epidemiology and Outcomes of Health-care-associated pneumonia. Chest. 2005;128(6):3854-3862. PubMed
5. Kalil AC, Metersky ML, Klompas M, et al. Management of Adults With Hospital-acquired and Ventilator-associated Pneumonia: 2016 Clinical Practice Guidelines by the Infectious Diseases Society of America and the American Thoracic Society. Clin Infect Dis. 2016;63(5):575-582. PubMed
6. Lindenauer PK, Pekow PS, Lahti MC, Lee Y, Benjamin EM, Rothberg MB. Association of corticosteroid dose and route of administration with risk of treatment failure in acute exacerbation of chronic obstructive pulmonary disease. JAMA. 2010;303(23):2359-2367. PubMed
7. Elixhauser A, Steiner C, Harris DR, Coffey RM. Comorbidity measures for use with administrative data. Med Care. 1998;36(1):8-27. PubMed
8. Centers for Medicare & Medicaid Services. Frequently asked questions (FAQs): Implementation and maintenance of CMS mortality measures for AMI & HF. 2007. https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/HospitalQualityInits/downloads/HospitalMortalityAboutAMI_HF.pdf. Accessed November 1, 2016.
9. Normand SL, Shahian DM. Statistical and Clinical Aspects of Hospital Outcomes Profiling. Stat Sci. 2007;22(2):206-226.
10. Rothberg MB, Pekow PS, Priya A, et al. Using highly detailed administrative data to predict pneumonia mortality. PLoS One. 2014;9(1):e87382. PubMed
11. Jones BE, Jones MM, Huttner B, et al. Trends in antibiotic use and nosocomial pathogens in hospitalized veterans with pneumonia at 128 medical centers, 2006-2010. Clin Infect Dis. 2015;61(9):1403-1410. PubMed
12. Attridge RT, Frei CR, Restrepo MI, et al. Guideline-concordant therapy and outcomes in healthcare-associated pneumonia. Eur Respir J. 2011;38(4):878-887. PubMed
13. Rothberg MB, Zilberberg MD, Pekow PS, et al. Association of Guideline-based Antimicrobial Therapy and Outcomes in Healthcare-Associated Pneumonia. J Antimicrob Chemother. 2015;70(5):1573-1579. PubMed
14. Kett DH, Cano E, Quartin AA, et al. Improving Medicine through Pathway Assessment of Critical Therapy of Hospital-Acquired Pneumonia (IMPACT-HAP) Investigators. Implementation of guidelines for management of possible multidrug-resistant pneumonia in intensive care: an observational, multicentre cohort study. Lancet Infect Dis. 2011;11(3):181-189. PubMed
15. El-Solh AA, Alhajhusain A, Saliba RG, Drinka P. Physicians’ Attitudes Toward Guidelines for the Treatment of Hospitalized Nursing-Home -Acquired Pneumonia. J Am Med Dir Assoc. 2011;12(4):270-276. PubMed
16. Tunis S, Hayward R, Wilson M, et al. Internists’ Attitudes about Clinical Practice Guidelines. Ann Intern Med. 1994;120(11):956-963. PubMed
17. Seymann GB, Di Francesco L, Sharpe B, et al. The HCAP Gap: Differences between Self-Reported Practice Patterns and Published Guidelines for Health Care-Associated Pneumonia. Clin Infect Dis. 2009;49(12):1868-1874. PubMed
18. Chalmers JD, Rother C, Salih W, Ewig S. Healthcare associated pneumonia does not accurately identify potentially resistant pathogens: a systematic review and meta-analysis. Clin Infect Dis. 2014;58(3):330-339. PubMed
19. Shorr A, Zilberberg MD, Reichley R, et al. Validation of a Clinical Score for Assessing the Risk of Resistant Pathogens in Patients with Pneumonia Presenting to the Emergency Department. Clin Infect Dis. 2012;54(2):193-198. PubMed
20. Aliberti S, Pasquale MD, Zanaboni AM, et al. Stratifying Risk Factors for Multidrug-Resistant Pathogens in Hospitalized Patients Coming from the Community with Pneumonia. Clin Infect Dis. 2012;54(4):470-478. PubMed
21. Schreiber MP, Chan CM, Shorr AF. Resistant Pathogens in Nonnosocomial Pneumonia and Respiratory Failure: Is it Time to Refine the Definition of Health-care-Associated Pneumonia? Chest. 2010;137(6):1283-1288. PubMed
22. Madaras-Kelly KJ, Remington RE, Fan VS, Sloan KL. Predicting antibiotic resistance to community-acquired pneumonia antibiotics in culture-positive patients with healthcare-associated pneumonia. J Hosp Med. 2012;7(3):195-202. PubMed
23. Shindo Y, Ito R, Kobayashi D, et al. Risk factors for drug-resistant pathogens in community-acquired and healthcare-associated pneumonia. Am J Respir Crit Care Med. 2013;188(8):985-995. PubMed
24. Metersky ML, Frei CR, Mortensen EM. Predictors of Pseudomonas and methicillin-resistant Staphylococcus aureus in hospitalized patients with healthcare-associated pneumonia. Respirology. 2016;21(1):157-163. PubMed
25. Webb BJ, Dascomb K, Stenehjem E, Dean N. Predicting risk of drug-resistant organisms in pneumonia: moving beyond the HCAP model. Respir Med. 2015;109(1):1-10. PubMed
1. Kochanek KD, Murphy SL, Xu JQ, Tejada-Vera B. Deaths: Final data for 2014. National vital statistics reports; vol 65 no 4. Hyattsville, MD: National Center for Health Statistics. 2016. PubMed
2. American Thoracic Society, Infectious Diseases Society of America. Guidelines for the Management of Adults with Hospital-acquired, Ventilator-associated, and Healthcare-associated Pneumonia. Am J Respir Crit Care Med. 2005;171(4):388-416. PubMed
3. Zilberberg MD, Shorr A. Healthcare-associated pneumonia: the state of the evidence to date. Curr Opin Pulm Med. 2011;17(3):142-147. PubMed
4. Kollef MK, Shorr A, Tabak YP, Gupta V, Liu LZ, Johannes RS. Epidemiology and Outcomes of Health-care-associated pneumonia. Chest. 2005;128(6):3854-3862. PubMed
5. Kalil AC, Metersky ML, Klompas M, et al. Management of Adults With Hospital-acquired and Ventilator-associated Pneumonia: 2016 Clinical Practice Guidelines by the Infectious Diseases Society of America and the American Thoracic Society. Clin Infect Dis. 2016;63(5):575-582. PubMed
6. Lindenauer PK, Pekow PS, Lahti MC, Lee Y, Benjamin EM, Rothberg MB. Association of corticosteroid dose and route of administration with risk of treatment failure in acute exacerbation of chronic obstructive pulmonary disease. JAMA. 2010;303(23):2359-2367. PubMed
7. Elixhauser A, Steiner C, Harris DR, Coffey RM. Comorbidity measures for use with administrative data. Med Care. 1998;36(1):8-27. PubMed
8. Centers for Medicare & Medicaid Services. Frequently asked questions (FAQs): Implementation and maintenance of CMS mortality measures for AMI & HF. 2007. https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/HospitalQualityInits/downloads/HospitalMortalityAboutAMI_HF.pdf. Accessed November 1, 2016.
9. Normand SL, Shahian DM. Statistical and Clinical Aspects of Hospital Outcomes Profiling. Stat Sci. 2007;22(2):206-226.
10. Rothberg MB, Pekow PS, Priya A, et al. Using highly detailed administrative data to predict pneumonia mortality. PLoS One. 2014;9(1):e87382. PubMed
11. Jones BE, Jones MM, Huttner B, et al. Trends in antibiotic use and nosocomial pathogens in hospitalized veterans with pneumonia at 128 medical centers, 2006-2010. Clin Infect Dis. 2015;61(9):1403-1410. PubMed
12. Attridge RT, Frei CR, Restrepo MI, et al. Guideline-concordant therapy and outcomes in healthcare-associated pneumonia. Eur Respir J. 2011;38(4):878-887. PubMed
13. Rothberg MB, Zilberberg MD, Pekow PS, et al. Association of Guideline-based Antimicrobial Therapy and Outcomes in Healthcare-Associated Pneumonia. J Antimicrob Chemother. 2015;70(5):1573-1579. PubMed
14. Kett DH, Cano E, Quartin AA, et al. Improving Medicine through Pathway Assessment of Critical Therapy of Hospital-Acquired Pneumonia (IMPACT-HAP) Investigators. Implementation of guidelines for management of possible multidrug-resistant pneumonia in intensive care: an observational, multicentre cohort study. Lancet Infect Dis. 2011;11(3):181-189. PubMed
15. El-Solh AA, Alhajhusain A, Saliba RG, Drinka P. Physicians’ Attitudes Toward Guidelines for the Treatment of Hospitalized Nursing-Home -Acquired Pneumonia. J Am Med Dir Assoc. 2011;12(4):270-276. PubMed
16. Tunis S, Hayward R, Wilson M, et al. Internists’ Attitudes about Clinical Practice Guidelines. Ann Intern Med. 1994;120(11):956-963. PubMed
17. Seymann GB, Di Francesco L, Sharpe B, et al. The HCAP Gap: Differences between Self-Reported Practice Patterns and Published Guidelines for Health Care-Associated Pneumonia. Clin Infect Dis. 2009;49(12):1868-1874. PubMed
18. Chalmers JD, Rother C, Salih W, Ewig S. Healthcare associated pneumonia does not accurately identify potentially resistant pathogens: a systematic review and meta-analysis. Clin Infect Dis. 2014;58(3):330-339. PubMed
19. Shorr A, Zilberberg MD, Reichley R, et al. Validation of a Clinical Score for Assessing the Risk of Resistant Pathogens in Patients with Pneumonia Presenting to the Emergency Department. Clin Infect Dis. 2012;54(2):193-198. PubMed
20. Aliberti S, Pasquale MD, Zanaboni AM, et al. Stratifying Risk Factors for Multidrug-Resistant Pathogens in Hospitalized Patients Coming from the Community with Pneumonia. Clin Infect Dis. 2012;54(4):470-478. PubMed
21. Schreiber MP, Chan CM, Shorr AF. Resistant Pathogens in Nonnosocomial Pneumonia and Respiratory Failure: Is it Time to Refine the Definition of Health-care-Associated Pneumonia? Chest. 2010;137(6):1283-1288. PubMed
22. Madaras-Kelly KJ, Remington RE, Fan VS, Sloan KL. Predicting antibiotic resistance to community-acquired pneumonia antibiotics in culture-positive patients with healthcare-associated pneumonia. J Hosp Med. 2012;7(3):195-202. PubMed
23. Shindo Y, Ito R, Kobayashi D, et al. Risk factors for drug-resistant pathogens in community-acquired and healthcare-associated pneumonia. Am J Respir Crit Care Med. 2013;188(8):985-995. PubMed
24. Metersky ML, Frei CR, Mortensen EM. Predictors of Pseudomonas and methicillin-resistant Staphylococcus aureus in hospitalized patients with healthcare-associated pneumonia. Respirology. 2016;21(1):157-163. PubMed
25. Webb BJ, Dascomb K, Stenehjem E, Dean N. Predicting risk of drug-resistant organisms in pneumonia: moving beyond the HCAP model. Respir Med. 2015;109(1):1-10. PubMed
© 2017 Society of Hospital Medicine
Secular Trends in AB Resistance
Among hospitalized patients with serious infections, the choice of empiric therapy plays a key role in outcomes.[1, 2, 3, 4, 5, 6, 7, 8, 9] Rising rates and variable patterns of antimicrobial resistance, however, complicate selecting appropriate empiric therapy. Amidst this shifting landscape of resistance to antimicrobials, gram‐negative bacteria and specifically Acinetobacter baumannii (AB), remain a considerable challenge.[10] On the one hand, AB is a less‐frequent cause of serious infections than organisms like Pseudomonas aeruginosa or Enterobacteriaceae in severely ill hospitalized patients.[11, 12] On the other, AB has evolved a variety of resistance mechanisms and exhibits unpredictable susceptibility patterns.[13] These factors combine to increase the likelihood of administering inappropriate empiric therapy when faced with an infection caused by AB and, thereby, raising the risk of death.[14] The fact that clinicians may not routinely consider AB as the potential culprit pathogen in the patient they are treating along with this organism's highly in vitro resistant nature, may result in routine gram‐negative coverage being frequently inadequate for AB infections.
To address the poor outcomes related to inappropriate empiric therapy in the setting of AB, one requires an appreciation of the longitudinal changes and geographic differences in the susceptibility of this pathogen. Thus, we aimed to examine secular trends in the resistance of AB to antimicrobial agents whose effectiveness against this microorganism was well supported in the literature during the study timeframe.[15]
METHODS
To determine the prevalence of predefined resistance patterns among AB in respiratory and blood stream infection (BSI) specimens, we examined The Surveillance Network (TSN) database from Eurofins. We explored data collected between years 2003 and 2012. The database has been used extensively for surveillance purposes since 1994, and has previously been described in detail.[16, 17, 18, 19, 20] Briefly, TSN is a warehouse of routine clinical microbiology data collected from a nationally representative sample of microbiology laboratories in 217 hospitals in the United States. To minimize selection bias, laboratories are included based on their geography and the demographics of the populations they serve.[18] Only clinically significant samples are reported. No personal identifying information for source patients is available in this database. Only source laboratories that perform antimicrobial susceptibility testing according standard Food and Drug Administrationapproved testing methods and that interpret susceptibility in accordance with the Clinical Laboratory Standards Institute breakpoints are included.[21] (See Supporting Table 4 in the online version of this article for minimum inhibitory concentration (MIC) changes over the course of the studycurrent colistin and polymyxin breakpoints applied retrospectively). All enrolled laboratories undergo a pre‐enrollment site visit. Logical filters are used for routine quality control to detect unusual susceptibility profiles and to ensure appropriate testing methods. Repeat testing and reporting are done as necessary.[18]
Laboratory samples are reported as susceptible, intermediate, or resistant. We grouped isolates with intermediate MICs together with the resistant ones for the purposes of the current analysis. Duplicate isolates were excluded. Only samples representing 1 of the 2 infections of interest, respiratory or BSI, were included.
We examined 3 time periods2003 to 2005, 2006 to 2008, and 2009 to 2012for the prevalence of AB's resistance to the following antibiotics: carbapenems (imipenem, meropenem, doripenem), aminoglycosides (tobramycin, amikacin), tetracyclines (minocycline, doxycycline), polymyxins (colistin, polymyxin B), ampicillin‐sulbactam, and trimethoprim‐sulfamethoxazole. Antimicrobial resistance was defined by the designation of intermediate or resistant in the susceptibility category. Resistance to a class of antibiotics was defined as resistance to all drugs within the class for which testing was available. The organism was multidrug resistant (MDR) if it was resistant to at least 1 antimicrobial in at least 3 drug classes examined.[22] Resistance to a combination of 2 drugs was present if the specimen was resistant to both of the drugs in the combination for which testing was available. We examined the data by infection type, time period, the 9 US Census divisions, and location of origin of the sample.
All categorical variables are reported as percentages. Continuous variables are reported as meansstandard deviations and/or medians with the interquartile range (IQR). We did not pursue hypothesis testing due to a high risk of type I error in this large dataset. Therefore, only clinically important trends are highlighted.
RESULTS
Among the 39,320 AB specimens, 81.1% were derived from a respiratory source and 18.9% represented BSI. Demographics of source patients are listed in Table 1. Notably, the median age of those with respiratory infection (58 years; IQR 38, 73) was higher than among patients with BSI (54.5 years; IQR 36, 71), and there were proportionally fewer females among respiratory patients (39.9%) than those with BSI (46.0%). Though only 24.3% of all BSI samples originated from the intensive are unit (ICU), 40.5% of respiratory specimens came from that location. The plurality of all specimens was collected in the 2003 to 2005 time interval (41.3%), followed by 2006 to 2008 (34.7%), with a minority coming from years 2009 to 2012 (24.0%). The proportions of collected specimens from respiratory and BSI sources were similar in all time periods examined (Table 1). Geographically, the South Atlantic division contributed the most samples (24.1%) and East South Central the fewest (2.6%) (Figure 1). The vast majority of all samples came from hospital wards (78.6%), where roughly one‐half originated in the ICU (37.5%). Fewer still came from outpatient sources (18.3%), and a small minority (2.5%) from nursing homes.
| Pneumonia | BSI | All | |
|---|---|---|---|
| |||
| Total, N (%) | 31,868 (81.1) | 7,452 (18.9) | 39,320 |
| Age, y | |||
| Mean (SD) | 57.7 (37.4) | 57.6 (40.6) | 57.7 (38.0) |
| Median (IQR 25, 75) | 58 (38, 73) | 54.5 (36, 71) | 57 (37, 73) |
| Gender, female (%) | 12,725 (39.9) | 3,425 (46.0) | 16,150 (41.1) |
| ICU (%) | 12,9191 (40.5) | 1,809 (24.3) | 14,7284 (37.5) |
| Time period, % total | |||
| 20032005 | 12,910 (40.5) | 3,340 (44.8) | 16,250 (41.3) |
| 20062008 | 11,205 (35.2) | 2,435 (32.7) | 13,640 (34.7) |
| 20092012 | 7,753 (24.3) | 1,677 (22.5) | 9,430 (24.0) |
Figure 2 depicts overall resistance patterns by individual drugs, drug classes, and frequently used combinations of agents. Although doripenem had the highest rate of resistance numerically (90.3%), its susceptibility was tested only in a small minority of specimens (n=31, 0.08%). Resistance to trimethoprim‐sulfamethoxazole was high (55.3%) based on a large number of samples tested (n=33,031). Conversely, colistin as an agent and polymyxins as a class exhibited the highest susceptibility rates of over 90%, though the numbers of samples tested for susceptibility to these drugs were also small (colistin n=2,086, 5.3%; polymyxins n=3,120, 7.9%) (Figure 2). Among commonly used drug combinations, carbapenem+aminoglycoside (18.0%) had the lowest resistance rates, and nearly 30% of all AB specimens tested met the criteria for MDR.
Over time, resistance to carbapenems more‐than doubled, from 21.0% in 2003 to 2005 to 47.9% in 2009 to 2012 (Table 2). Although relatively few samples were tested for colistin susceptibility (n=2,086, 5.3%), resistance to this drug also more than doubled from 2.8% (95% confidence interval: 1.9‐4.2) in 2006 to 2008 to 6.9% (95% confidence interval: 5.7‐8.2) in 2009 to 2012. As a class, however, polymyxins exhibited stable resistance rates over the time frame of the study (Table 2). Prevalence of MDR AB rose from 21.4% in 2003 to 2005 to 33.7% in 2006 to 2008, and remained stable at 35.2% in 2009 to 2012. Resistance to even such broad combinations as carbapenem+ampicillin/sulbactam nearly tripled from 13.2% in 2003 to 2005 to 35.5% in 2009 to 2012. Notably, between 2003 and 2012, although resistance rates either rose or remained stable to all other agents, those to minocycline diminished from 56.5% in 2003 to 2005 to 36.6% in 2006 to 2008 to 30.5% in 2009 to 2012. (See Supporting Table 1 in the online version of this article for time trends based on whether they represented respiratory or BSI specimens, with directionally similar trends in both.)
| Drug/Combination | Time Period | ||||||||
|---|---|---|---|---|---|---|---|---|---|
| 20032005 | 20062008 | 20092012 | |||||||
| Na | %b | 95% CI | N | % | 95% CI | N | % | 95% CI | |
| |||||||||
| Amikacin | 12,949 | 25.2 | 24.5‐26.0 | 10.929 | 35.2 | 34.3‐36.1 | 6,292 | 45.7 | 44.4‐46.9 |
| Tobramycin | 14,549 | 37.1 | 36.3‐37.9 | 11,877 | 41.9 | 41.0‐42.8 | 7,901 | 39.2 | 38.1‐40.3 |
| Aminoglycoside | 14,505 | 22.5 | 21.8‐23.2 | 11,967 | 30.6 | 29.8‐31.4 | 7,736 | 34.8 | 33.8‐35.8 |
| Doxycycline | 173 | 36.4 | 29.6‐43.8 | 38 | 29.0 | 17.0‐44.8 | 32 | 34.4 | 20.4‐51.7 |
| Minocycline | 1,388 | 56.5 | 53.9‐50.1 | 902 | 36.6 | 33.5‐39.8 | 522 | 30.5 | 26.7‐34.5 |
| Tetracycline | 1,511 | 55.4 | 52.9‐57.9 | 940 | 36.3 | 33.3‐39.4 | 546 | 30.8 | 27.0‐34.8 |
| Doripenem | NR | NR | NR | 9 | 77.8 | 45.3‐93.7 | 22 | 95.5 | 78.2‐99.2 |
| Imipenem | 14,728 | 21.8 | 21.2‐22.5 | 12,094 | 40.3 | 39.4‐41.2 | 6,681 | 51.7 | 50.5‐52.9 |
| Meropenem | 7,226 | 37.0 | 35.9‐38.1 | 5,628 | 48.7 | 47.3‐50.0 | 4,919 | 47.3 | 45.9‐48.7 |
| Carbapenem | 15,490 | 21.0 | 20.4‐21.7 | 12,975 | 38.8 | 38.0‐39.7 | 8,778 | 47.9 | 46.9‐49.0 |
| Ampicillin/sulbactam | 10,525 | 35.2 | 34.3‐36.2 | 9,413 | 44.9 | 43.9‐45.9 | 6,460 | 41.2 | 40.0‐42.4 |
| Colistin | NR | NR | NR | 783 | 2.8 | 1.9‐4.2 | 1,303 | 6.9 | 5.7‐8.2 |
| Polymyxin B | 105 | 7.6 | 3.9‐14.3 | 796 | 12.8 | 10.7‐15.3 | 321 | 6.5 | 4.3‐9.6 |
| Polymyxin | 105 | 7.6 | 3.9‐14.3 | 1,563 | 7.9 | 6.6‐9.3 | 1,452 | 6.8 | 5.6‐8.2 |
| Trimethoprim/sulfamethoxazole | 13,640 | 52.5 | 51.7‐53.3 | 11,535 | 57.1 | 56.2‐58.0 | 7,856 | 57.6 | 56.5‐58.7 |
| MDRc | 16,249 | 21.4 | 20.7‐22.0 | 13,640 | 33.7 | 33.0‐34.5 | 9,431 | 35.2 | 34.2‐36.2 |
| Carbapenem+aminoglycoside | 14,601 | 8.9 | 8.5‐9.4 | 12,333 | 21.3 | 20.6‐22.0 | 8,256 | 29.3 | 28.3‐30.3 |
| Aminoglycoside+ampicillin/sulbactam | 10,107 | 12.9 | 12.3‐13.6 | 9,077 | 24.9 | 24.0‐25.8 | 6,200 | 24.3 | 23.2‐25.3 |
| Aminoglycosie+minocycline | 1,359 | 35.6 | 33.1‐38.2 | 856 | 21.4 | 18.8‐24.2 | 503 | 24.5 | 20.9‐28.4 |
| Carbapenem+ampicillin/sulbactam | 10,228 | 13.2 | 12.5‐13.9 | 9,145 | 29.4 | 28.4‐30.3 | 6,143 | 35.5 | 34.3‐36.7 |
Regionally, examining resistance by classes and combinations of antibiotics, trimethoprim‐sulfamethoxazole exhibited consistently the highest rates of resistance, ranging from the lowest in the New England (28.8%) to the highest in the East North Central (69.9%) Census divisions (See Supporting Table 2 in the online version of this article). The rates of resistance to tetracyclines ranged from 0.0% in New England to 52.6% in the Mountain division, and to polymyxins from 0.0% in the East South Central division to 23.4% in New England. Generally, New England enjoyed the lowest rates of resistance (0.0% to tetracyclines to 28.8% to trimethoprim‐sulfamethoxazole), and the Mountain division the highest (0.9% to polymyxins to 52.6% to tetracyclines). The rates of MDR AB ranged from 8.0% in New England to 50.4% in the Mountain division (see Supporting Table 2 in the online version of this article).
Examining resistances to drug classes and combinations by the location of the source specimen revealed that trimethoprim‐sulfamethoxazole once again exhibited the highest rate of resistance across all locations (see Supporting Table 3 in the online version of this article). Despite their modest contribution to the overall sample pool (n=967, 2.5%), organisms from nursing home subjects had the highest prevalence of resistance to aminoglycosides (36.3%), tetracyclines (57.1%), and carbapenems (47.1%). This pattern held true for combination regimens examined. Nursing homes also vastly surpassed other locations in the rates of MDR AB (46.5%). Interestingly, the rates of MDR did not differ substantially among regular inpatient wards (29.2%), the ICU (28.7%), and outpatient locations (26.2%) (see Supporting Table 3 in the online version of this article).
DISCUSSION
In this large multicenter survey we have documented the rising rates of AB resistance to clinically important antimicrobials in the United States. On the whole, all antimicrobials, except for minocycline, exhibited either large or small increases in resistance. Alarmingly, even colistin, a true last resort AB treatment, lost a considerable amount of activity against AB, with the resistance rate rising from 2.8% in 2006 to 2008 to 6.9% in 2009 to 2012. The single encouraging trend that we observed was that resistance to minocycline appeared to diminish substantially, going from over one‐half of all AB tested in 2003 to 2005 to just under one‐third in 2009 to 2012.
Although we did note a rise in the MDR AB, our data suggest a lower percentage of all AB that meets the MDR phenotype criteria compared to reports by other groups. For example, the Center for Disease Dynamics and Economic Policy (CDDEP), analyzing the same data as our study, reports a rise in MDR AB from 32.1% in 1999 to 51.0% in 2010.[23] This discrepancy is easily explained by the fact that we included polymyxins, tetracyclines, and trimethoprim‐sulfamethoxazole in our evaluation, whereas the CDDEP did not examine these agents. Furthermore, we omitted fluoroquinolones, a drug class with high rates of resistance, from our study, because we were interested in focusing only on antimicrobials with clinical data in AB infections.[22] In addition, we limited our evaluation to specimens derived from respiratory or BSI sources, whereas the CDDEP data reflect any AB isolate present in TSN.
We additionally confirm that there is substantial geographic variation in resistance patterns. Thus, despite different definitions, our data agree with those from the CDDEP that the MDR prevalence is highest in the Mountain and East North Central divisions, and lowest in New England overall.[23] The wide variations underscore the fact that it is not valid to speak of national rates of resistance, but rather it is important to concentrate on the local patterns. This information, though important from the macroepidemiologic standpoint, is likely still not granular enough to help clinicians make empiric treatment decisions. In fact, what is needed for that is real‐time antibiogram data specific to each center and even each unit within each center.
The latter point is further illustrated by our analysis of locations of origin of the specimens. In this analysis, we discovered that, contrary to the common presumption that the ICU has the highest rate of resistant organisms, specimens derived from nursing homes represent perhaps the most intensely resistant organisms. In other words, the nursing home is the setting most likely to harbor patients with respiratory infections and BSIs caused by resistant AB. These data are in agreement with several other recent investigations. In a period‐prevalence survey conducted in the state of Maryland in 2009 by Thom and colleagues, long‐term care facilities were found to have the highest prevalence of any AB, and also those resistant to imipenem, MDR, and extensively drug‐resistant organisms.[24] Mortensen and coworkers confirmed the high prevalence of AB and AB resistance in long‐term care facilities, and extended this finding to suggest that there is evidence for intra‐ and interhospital spread of these pathogens.[25] Our data confirm this concerning finding at the national level, and point to a potential area of intervention for infection prevention.
An additional finding of some concern is that the highest proportion of colistin resistance among those specimens, whose location of origin was reported in the database, was the outpatient setting (6.6% compared to 5.4% in the ICU specimens, for example). Although these infections would likely meet the definition for healthcare‐associated infection, AB as a community‐acquired respiratory pathogen is not unprecedented either in the United States or abroad.[26, 27, 28, 29, 30] It is, however, reassuring that most other antimicrobials examined in our study exhibit higher rates of susceptibility in the specimens derived from the outpatient settings than either from the hospital or the nursing home.
Our study has a number of strengths. As a large multicenter survey, it is representative of AB susceptibility patterns across the United States, which makes it highly generalizable. We focused on antibiotics for which clinical evidence is available, thus adding a practical dimension to the results. Another pragmatic consideration is examining the data by geographic distributions, allowing an additional layer of granularity for clinical decisions. At the same time it suffers from some limitations. The TSN database consists of microbiology samples from hospital laboratories. Although we attempted to reduce the risk of duplication, because of how samples are numbered in the database, repeat sampling remains a possibility. Despite having stratified the data by geography and the location of origin of the specimen, it is likely not granular enough for local risk stratification decisions clinicians make daily about the choices of empiric therapy. Some of the MIC breakpoints have changed over the period of the study (see Supporting Table 4 in the online version of this article). Because these changes occurred in the last year of data collection (2012), they should have had only a minimal, if any, impact on the observed rates of resistance in the time frame examined. Additionally, because resistance rates evolve rapidly, more current data are required for effective clinical decision making.
In summary, we have demonstrated that the last decade has seen an alarming increase in the rate of resistance of AB to multiple clinically important antimicrobial agents and classes. We have further emphasized the importance of granularity in susceptibility data to help clinicians make sensible decisions about empiric therapy in hospitalized patients with serious infections. Finally, and potentially most disturbingly, the nursing home as a location appears to be a robust reservoir for spread for resistant AB. All of these observations highlight the urgent need to develop novel antibiotics and nontraditional agents, such as antibodies and vaccines, to combat AB infections, in addition to having important infection prevention implications if we are to contain the looming threat of the end of antibiotics.[31]
Disclosure
This study was funded by a grant from Tetraphase Pharmaceuticals, Watertown, MA.
- National Nosocomial Infections Surveillance (NNIS) System Report. Am J Infect Control. 2004;32:470–485.
- , , , National surveillance of antimicrobial resistance in Pseudomonas aeruginosa isolates obtained from intensive care unit patients from 1993 to 2002. Antimicrob Agents Chemother. 2004;48:4606–4610.
- , , , et al. Health care‐associated pneumonia and community‐acquired pneumonia: a single‐center experience. Antimicrob Agents Chemother. 2007;51:3568–3573.
- , , , et al. Clinical importance of delays in the initiation of appropriate antibiotic treatment for ventilator‐associated pneumonia. Chest. 2002;122:262–268.
- ; ICU‐Acquired Pneumonia Study Group. Modification of empiric antibiotic treatment in patients with pneumonia acquired in the intensive care unit. Intensive Care Med. 1996;22:387–394.
- , , , , Antimicrobial therapy escalation and hospital mortality among patients with HCAP: a single center experience. Chest. 2008:134:963–968.
- , , , et al. Surviving Sepsis Campaign: international guidelines for management of severe sepsis and septic shock: 2008. Crit Care Med. 2008;36:296–327.
- , , , , , Inappropriate antibiotic therapy in Gram‐negative sepsis increases hospital length of stay. Crit Care Med. 2011;39:46–51.
- , , , Inadequate antimicrobial treatment of infections: a risk factor for hospital mortality among critically ill patients. Chest. 1999;115:462–474.
- Centers for Disease Control and Prevention. Antibiotic resistance threats in the United States, 2013. Available at: http://www.cdc.gov/drugresistance/threat-report-2013/pdf/ar-threats-2013-508.pdf#page=59. Accessed December 29, 2014.
- , , , et al.; National Healthcare Safety Network (NHSN) Team and Participating NHSN Facilities. Antimicrobial‐resistant pathogens associated with healthcare‐associated infections: summary of data reported to the National Healthcare Safety Network at the Centers for Disease Control and Prevention, 2009–2010. Infect Control Hosp Epidemiol. 2013;34:1–14.
- , , , , Multi‐drug resistance, inappropriate initial antibiotic therapy and mortality in Gram‐negative severe sepsis and septic shock: a retrospective cohort study. Crit Care. 2014;18(6):596.
- , , , , , Global challenge of multidrug‐resistant Acinetobacter baumannii. Antimicrob Agents Chemother. 2007;51:3471–3484.
- , , , Predictors of hospital mortality among septic ICU patients with Acinetobacter spp. bacteremia: a cohort study. BMC Infect Dis. 2014;14:572.
- , Treatment of Acinetobacter infections. Clin Infect Dis. 2010;51:79–84.
- , , Increasing resistance of Acinetobacter species to imipenem in United States hospitals, 1999–2006. Infect Control Hosp Epidemiol. 2010;31:196–197.
- , , , , Trends in resistance to carbapenems and third‐generation cephalosporins among clinical isolates of Klebsiella pneumoniae in the United States, 1999–2010. Infect Control Hosp Epidemiol. 2013;34:259–268.
- , , Antimicrobial resistance in key bloodstream bacterial isolates: electronic surveillance with the Surveillance Network Database—USA. Clin Infect Dis. 1999;29:259–263.
- , , Community‐associated methicillin‐resistant Staphylococcus aureus in outpatients, United States, 1999–2006. Emerg Infect Dis. 2009;15:1925–1930.
- , , , , Prevalence of antimicrobial resistance in bacteria isolated from central nervous system specimens as reported by U.S. hospital laboratories from 2000 to 2002. Ann Clin Microbiol Antimicrob. 2004;3:3.
- Performance standards for antimicrobial susceptibility testing: twenty‐second informational supplement. CLSI document M100‐S22. Wayne, PA: Clinical and Laboratory Standards Institute; 2012.
- , , , et al. Multidrug‐resistant, extensively drug‐resistant and pandrug‐resistant bacteria: an international expert proposal for interim standard definitions for acquired resistance. Clin Microbiol Infect. 2012;18:268–281.
- CDDEP: The Center for Disease Dynamics, Economics and Policy. Resistance map: Acinetobacter baumannii overview. Available at: http://www.cddep.org/projects/resistance_map/acinetobacter_baumannii_overview. Accessed January 16, 2015.
- , , , et al.; Maryland MDRO Prevention Collaborative. Assessing the burden of Acinetobacter baumannii in Maryland: a statewide cross‐sectional period prevalence survey. Infect Control Hosp Epidemiol. 2012;33:883–888.
- , , , et al. Multidrug‐resistant Acinetobacter baumannii infection, colonization, and transmission related to a long‐term care facility providing subacute care. Infect Control Hosp Epidemiol. 2014;35:406–411.
- , , , , , Severe community‐acquired pneumonia due to Acinetobacter baumannii. Chest. 2001;120:1072–1077.
- , , , , , Fulminant community‐acquired Acinetobacter baumannii pneumonia as distinct clinical syndrome. Chest. 2006;129:102–109.
- , , , Community‐acquired Acinetobacter baumannii pneumonia. Rev Clin Esp. 2003;203:284–286.
- , , , et al. Antimicrobial drug‐resistant microbes associated with hospitalized community‐acquired and healthcare‐associated pneumonia: a multi‐center study in Taiwan. J Formos Med Assoc. 2013;112:31–40.
- , , , , Antimicrobial resistance in Hispanic patients hospitalized in San Antonio, TX with community‐acquired pneumonia. Hosp Pract (1995). 2010;38:108–113.
- Centers for Disease Control and Prevention. CDC director blog. The end of antibiotics. Can we come back from the brink? Available at: http://blogs.cdc.gov/cdcdirector/2014/05/05/the-end-of-antibiotics-can-we-come-back-from-the-brink/. Published May 5, 2014. Accessed January 16, 2015.
Among hospitalized patients with serious infections, the choice of empiric therapy plays a key role in outcomes.[1, 2, 3, 4, 5, 6, 7, 8, 9] Rising rates and variable patterns of antimicrobial resistance, however, complicate selecting appropriate empiric therapy. Amidst this shifting landscape of resistance to antimicrobials, gram‐negative bacteria and specifically Acinetobacter baumannii (AB), remain a considerable challenge.[10] On the one hand, AB is a less‐frequent cause of serious infections than organisms like Pseudomonas aeruginosa or Enterobacteriaceae in severely ill hospitalized patients.[11, 12] On the other, AB has evolved a variety of resistance mechanisms and exhibits unpredictable susceptibility patterns.[13] These factors combine to increase the likelihood of administering inappropriate empiric therapy when faced with an infection caused by AB and, thereby, raising the risk of death.[14] The fact that clinicians may not routinely consider AB as the potential culprit pathogen in the patient they are treating along with this organism's highly in vitro resistant nature, may result in routine gram‐negative coverage being frequently inadequate for AB infections.
To address the poor outcomes related to inappropriate empiric therapy in the setting of AB, one requires an appreciation of the longitudinal changes and geographic differences in the susceptibility of this pathogen. Thus, we aimed to examine secular trends in the resistance of AB to antimicrobial agents whose effectiveness against this microorganism was well supported in the literature during the study timeframe.[15]
METHODS
To determine the prevalence of predefined resistance patterns among AB in respiratory and blood stream infection (BSI) specimens, we examined The Surveillance Network (TSN) database from Eurofins. We explored data collected between years 2003 and 2012. The database has been used extensively for surveillance purposes since 1994, and has previously been described in detail.[16, 17, 18, 19, 20] Briefly, TSN is a warehouse of routine clinical microbiology data collected from a nationally representative sample of microbiology laboratories in 217 hospitals in the United States. To minimize selection bias, laboratories are included based on their geography and the demographics of the populations they serve.[18] Only clinically significant samples are reported. No personal identifying information for source patients is available in this database. Only source laboratories that perform antimicrobial susceptibility testing according standard Food and Drug Administrationapproved testing methods and that interpret susceptibility in accordance with the Clinical Laboratory Standards Institute breakpoints are included.[21] (See Supporting Table 4 in the online version of this article for minimum inhibitory concentration (MIC) changes over the course of the studycurrent colistin and polymyxin breakpoints applied retrospectively). All enrolled laboratories undergo a pre‐enrollment site visit. Logical filters are used for routine quality control to detect unusual susceptibility profiles and to ensure appropriate testing methods. Repeat testing and reporting are done as necessary.[18]
Laboratory samples are reported as susceptible, intermediate, or resistant. We grouped isolates with intermediate MICs together with the resistant ones for the purposes of the current analysis. Duplicate isolates were excluded. Only samples representing 1 of the 2 infections of interest, respiratory or BSI, were included.
We examined 3 time periods2003 to 2005, 2006 to 2008, and 2009 to 2012for the prevalence of AB's resistance to the following antibiotics: carbapenems (imipenem, meropenem, doripenem), aminoglycosides (tobramycin, amikacin), tetracyclines (minocycline, doxycycline), polymyxins (colistin, polymyxin B), ampicillin‐sulbactam, and trimethoprim‐sulfamethoxazole. Antimicrobial resistance was defined by the designation of intermediate or resistant in the susceptibility category. Resistance to a class of antibiotics was defined as resistance to all drugs within the class for which testing was available. The organism was multidrug resistant (MDR) if it was resistant to at least 1 antimicrobial in at least 3 drug classes examined.[22] Resistance to a combination of 2 drugs was present if the specimen was resistant to both of the drugs in the combination for which testing was available. We examined the data by infection type, time period, the 9 US Census divisions, and location of origin of the sample.
All categorical variables are reported as percentages. Continuous variables are reported as meansstandard deviations and/or medians with the interquartile range (IQR). We did not pursue hypothesis testing due to a high risk of type I error in this large dataset. Therefore, only clinically important trends are highlighted.
RESULTS
Among the 39,320 AB specimens, 81.1% were derived from a respiratory source and 18.9% represented BSI. Demographics of source patients are listed in Table 1. Notably, the median age of those with respiratory infection (58 years; IQR 38, 73) was higher than among patients with BSI (54.5 years; IQR 36, 71), and there were proportionally fewer females among respiratory patients (39.9%) than those with BSI (46.0%). Though only 24.3% of all BSI samples originated from the intensive are unit (ICU), 40.5% of respiratory specimens came from that location. The plurality of all specimens was collected in the 2003 to 2005 time interval (41.3%), followed by 2006 to 2008 (34.7%), with a minority coming from years 2009 to 2012 (24.0%). The proportions of collected specimens from respiratory and BSI sources were similar in all time periods examined (Table 1). Geographically, the South Atlantic division contributed the most samples (24.1%) and East South Central the fewest (2.6%) (Figure 1). The vast majority of all samples came from hospital wards (78.6%), where roughly one‐half originated in the ICU (37.5%). Fewer still came from outpatient sources (18.3%), and a small minority (2.5%) from nursing homes.
| Pneumonia | BSI | All | |
|---|---|---|---|
| |||
| Total, N (%) | 31,868 (81.1) | 7,452 (18.9) | 39,320 |
| Age, y | |||
| Mean (SD) | 57.7 (37.4) | 57.6 (40.6) | 57.7 (38.0) |
| Median (IQR 25, 75) | 58 (38, 73) | 54.5 (36, 71) | 57 (37, 73) |
| Gender, female (%) | 12,725 (39.9) | 3,425 (46.0) | 16,150 (41.1) |
| ICU (%) | 12,9191 (40.5) | 1,809 (24.3) | 14,7284 (37.5) |
| Time period, % total | |||
| 20032005 | 12,910 (40.5) | 3,340 (44.8) | 16,250 (41.3) |
| 20062008 | 11,205 (35.2) | 2,435 (32.7) | 13,640 (34.7) |
| 20092012 | 7,753 (24.3) | 1,677 (22.5) | 9,430 (24.0) |
Figure 2 depicts overall resistance patterns by individual drugs, drug classes, and frequently used combinations of agents. Although doripenem had the highest rate of resistance numerically (90.3%), its susceptibility was tested only in a small minority of specimens (n=31, 0.08%). Resistance to trimethoprim‐sulfamethoxazole was high (55.3%) based on a large number of samples tested (n=33,031). Conversely, colistin as an agent and polymyxins as a class exhibited the highest susceptibility rates of over 90%, though the numbers of samples tested for susceptibility to these drugs were also small (colistin n=2,086, 5.3%; polymyxins n=3,120, 7.9%) (Figure 2). Among commonly used drug combinations, carbapenem+aminoglycoside (18.0%) had the lowest resistance rates, and nearly 30% of all AB specimens tested met the criteria for MDR.
Over time, resistance to carbapenems more‐than doubled, from 21.0% in 2003 to 2005 to 47.9% in 2009 to 2012 (Table 2). Although relatively few samples were tested for colistin susceptibility (n=2,086, 5.3%), resistance to this drug also more than doubled from 2.8% (95% confidence interval: 1.9‐4.2) in 2006 to 2008 to 6.9% (95% confidence interval: 5.7‐8.2) in 2009 to 2012. As a class, however, polymyxins exhibited stable resistance rates over the time frame of the study (Table 2). Prevalence of MDR AB rose from 21.4% in 2003 to 2005 to 33.7% in 2006 to 2008, and remained stable at 35.2% in 2009 to 2012. Resistance to even such broad combinations as carbapenem+ampicillin/sulbactam nearly tripled from 13.2% in 2003 to 2005 to 35.5% in 2009 to 2012. Notably, between 2003 and 2012, although resistance rates either rose or remained stable to all other agents, those to minocycline diminished from 56.5% in 2003 to 2005 to 36.6% in 2006 to 2008 to 30.5% in 2009 to 2012. (See Supporting Table 1 in the online version of this article for time trends based on whether they represented respiratory or BSI specimens, with directionally similar trends in both.)
| Drug/Combination | Time Period | ||||||||
|---|---|---|---|---|---|---|---|---|---|
| 20032005 | 20062008 | 20092012 | |||||||
| Na | %b | 95% CI | N | % | 95% CI | N | % | 95% CI | |
| |||||||||
| Amikacin | 12,949 | 25.2 | 24.5‐26.0 | 10.929 | 35.2 | 34.3‐36.1 | 6,292 | 45.7 | 44.4‐46.9 |
| Tobramycin | 14,549 | 37.1 | 36.3‐37.9 | 11,877 | 41.9 | 41.0‐42.8 | 7,901 | 39.2 | 38.1‐40.3 |
| Aminoglycoside | 14,505 | 22.5 | 21.8‐23.2 | 11,967 | 30.6 | 29.8‐31.4 | 7,736 | 34.8 | 33.8‐35.8 |
| Doxycycline | 173 | 36.4 | 29.6‐43.8 | 38 | 29.0 | 17.0‐44.8 | 32 | 34.4 | 20.4‐51.7 |
| Minocycline | 1,388 | 56.5 | 53.9‐50.1 | 902 | 36.6 | 33.5‐39.8 | 522 | 30.5 | 26.7‐34.5 |
| Tetracycline | 1,511 | 55.4 | 52.9‐57.9 | 940 | 36.3 | 33.3‐39.4 | 546 | 30.8 | 27.0‐34.8 |
| Doripenem | NR | NR | NR | 9 | 77.8 | 45.3‐93.7 | 22 | 95.5 | 78.2‐99.2 |
| Imipenem | 14,728 | 21.8 | 21.2‐22.5 | 12,094 | 40.3 | 39.4‐41.2 | 6,681 | 51.7 | 50.5‐52.9 |
| Meropenem | 7,226 | 37.0 | 35.9‐38.1 | 5,628 | 48.7 | 47.3‐50.0 | 4,919 | 47.3 | 45.9‐48.7 |
| Carbapenem | 15,490 | 21.0 | 20.4‐21.7 | 12,975 | 38.8 | 38.0‐39.7 | 8,778 | 47.9 | 46.9‐49.0 |
| Ampicillin/sulbactam | 10,525 | 35.2 | 34.3‐36.2 | 9,413 | 44.9 | 43.9‐45.9 | 6,460 | 41.2 | 40.0‐42.4 |
| Colistin | NR | NR | NR | 783 | 2.8 | 1.9‐4.2 | 1,303 | 6.9 | 5.7‐8.2 |
| Polymyxin B | 105 | 7.6 | 3.9‐14.3 | 796 | 12.8 | 10.7‐15.3 | 321 | 6.5 | 4.3‐9.6 |
| Polymyxin | 105 | 7.6 | 3.9‐14.3 | 1,563 | 7.9 | 6.6‐9.3 | 1,452 | 6.8 | 5.6‐8.2 |
| Trimethoprim/sulfamethoxazole | 13,640 | 52.5 | 51.7‐53.3 | 11,535 | 57.1 | 56.2‐58.0 | 7,856 | 57.6 | 56.5‐58.7 |
| MDRc | 16,249 | 21.4 | 20.7‐22.0 | 13,640 | 33.7 | 33.0‐34.5 | 9,431 | 35.2 | 34.2‐36.2 |
| Carbapenem+aminoglycoside | 14,601 | 8.9 | 8.5‐9.4 | 12,333 | 21.3 | 20.6‐22.0 | 8,256 | 29.3 | 28.3‐30.3 |
| Aminoglycoside+ampicillin/sulbactam | 10,107 | 12.9 | 12.3‐13.6 | 9,077 | 24.9 | 24.0‐25.8 | 6,200 | 24.3 | 23.2‐25.3 |
| Aminoglycosie+minocycline | 1,359 | 35.6 | 33.1‐38.2 | 856 | 21.4 | 18.8‐24.2 | 503 | 24.5 | 20.9‐28.4 |
| Carbapenem+ampicillin/sulbactam | 10,228 | 13.2 | 12.5‐13.9 | 9,145 | 29.4 | 28.4‐30.3 | 6,143 | 35.5 | 34.3‐36.7 |
Regionally, examining resistance by classes and combinations of antibiotics, trimethoprim‐sulfamethoxazole exhibited consistently the highest rates of resistance, ranging from the lowest in the New England (28.8%) to the highest in the East North Central (69.9%) Census divisions (See Supporting Table 2 in the online version of this article). The rates of resistance to tetracyclines ranged from 0.0% in New England to 52.6% in the Mountain division, and to polymyxins from 0.0% in the East South Central division to 23.4% in New England. Generally, New England enjoyed the lowest rates of resistance (0.0% to tetracyclines to 28.8% to trimethoprim‐sulfamethoxazole), and the Mountain division the highest (0.9% to polymyxins to 52.6% to tetracyclines). The rates of MDR AB ranged from 8.0% in New England to 50.4% in the Mountain division (see Supporting Table 2 in the online version of this article).
Examining resistances to drug classes and combinations by the location of the source specimen revealed that trimethoprim‐sulfamethoxazole once again exhibited the highest rate of resistance across all locations (see Supporting Table 3 in the online version of this article). Despite their modest contribution to the overall sample pool (n=967, 2.5%), organisms from nursing home subjects had the highest prevalence of resistance to aminoglycosides (36.3%), tetracyclines (57.1%), and carbapenems (47.1%). This pattern held true for combination regimens examined. Nursing homes also vastly surpassed other locations in the rates of MDR AB (46.5%). Interestingly, the rates of MDR did not differ substantially among regular inpatient wards (29.2%), the ICU (28.7%), and outpatient locations (26.2%) (see Supporting Table 3 in the online version of this article).
DISCUSSION
In this large multicenter survey we have documented the rising rates of AB resistance to clinically important antimicrobials in the United States. On the whole, all antimicrobials, except for minocycline, exhibited either large or small increases in resistance. Alarmingly, even colistin, a true last resort AB treatment, lost a considerable amount of activity against AB, with the resistance rate rising from 2.8% in 2006 to 2008 to 6.9% in 2009 to 2012. The single encouraging trend that we observed was that resistance to minocycline appeared to diminish substantially, going from over one‐half of all AB tested in 2003 to 2005 to just under one‐third in 2009 to 2012.
Although we did note a rise in the MDR AB, our data suggest a lower percentage of all AB that meets the MDR phenotype criteria compared to reports by other groups. For example, the Center for Disease Dynamics and Economic Policy (CDDEP), analyzing the same data as our study, reports a rise in MDR AB from 32.1% in 1999 to 51.0% in 2010.[23] This discrepancy is easily explained by the fact that we included polymyxins, tetracyclines, and trimethoprim‐sulfamethoxazole in our evaluation, whereas the CDDEP did not examine these agents. Furthermore, we omitted fluoroquinolones, a drug class with high rates of resistance, from our study, because we were interested in focusing only on antimicrobials with clinical data in AB infections.[22] In addition, we limited our evaluation to specimens derived from respiratory or BSI sources, whereas the CDDEP data reflect any AB isolate present in TSN.
We additionally confirm that there is substantial geographic variation in resistance patterns. Thus, despite different definitions, our data agree with those from the CDDEP that the MDR prevalence is highest in the Mountain and East North Central divisions, and lowest in New England overall.[23] The wide variations underscore the fact that it is not valid to speak of national rates of resistance, but rather it is important to concentrate on the local patterns. This information, though important from the macroepidemiologic standpoint, is likely still not granular enough to help clinicians make empiric treatment decisions. In fact, what is needed for that is real‐time antibiogram data specific to each center and even each unit within each center.
The latter point is further illustrated by our analysis of locations of origin of the specimens. In this analysis, we discovered that, contrary to the common presumption that the ICU has the highest rate of resistant organisms, specimens derived from nursing homes represent perhaps the most intensely resistant organisms. In other words, the nursing home is the setting most likely to harbor patients with respiratory infections and BSIs caused by resistant AB. These data are in agreement with several other recent investigations. In a period‐prevalence survey conducted in the state of Maryland in 2009 by Thom and colleagues, long‐term care facilities were found to have the highest prevalence of any AB, and also those resistant to imipenem, MDR, and extensively drug‐resistant organisms.[24] Mortensen and coworkers confirmed the high prevalence of AB and AB resistance in long‐term care facilities, and extended this finding to suggest that there is evidence for intra‐ and interhospital spread of these pathogens.[25] Our data confirm this concerning finding at the national level, and point to a potential area of intervention for infection prevention.
An additional finding of some concern is that the highest proportion of colistin resistance among those specimens, whose location of origin was reported in the database, was the outpatient setting (6.6% compared to 5.4% in the ICU specimens, for example). Although these infections would likely meet the definition for healthcare‐associated infection, AB as a community‐acquired respiratory pathogen is not unprecedented either in the United States or abroad.[26, 27, 28, 29, 30] It is, however, reassuring that most other antimicrobials examined in our study exhibit higher rates of susceptibility in the specimens derived from the outpatient settings than either from the hospital or the nursing home.
Our study has a number of strengths. As a large multicenter survey, it is representative of AB susceptibility patterns across the United States, which makes it highly generalizable. We focused on antibiotics for which clinical evidence is available, thus adding a practical dimension to the results. Another pragmatic consideration is examining the data by geographic distributions, allowing an additional layer of granularity for clinical decisions. At the same time it suffers from some limitations. The TSN database consists of microbiology samples from hospital laboratories. Although we attempted to reduce the risk of duplication, because of how samples are numbered in the database, repeat sampling remains a possibility. Despite having stratified the data by geography and the location of origin of the specimen, it is likely not granular enough for local risk stratification decisions clinicians make daily about the choices of empiric therapy. Some of the MIC breakpoints have changed over the period of the study (see Supporting Table 4 in the online version of this article). Because these changes occurred in the last year of data collection (2012), they should have had only a minimal, if any, impact on the observed rates of resistance in the time frame examined. Additionally, because resistance rates evolve rapidly, more current data are required for effective clinical decision making.
In summary, we have demonstrated that the last decade has seen an alarming increase in the rate of resistance of AB to multiple clinically important antimicrobial agents and classes. We have further emphasized the importance of granularity in susceptibility data to help clinicians make sensible decisions about empiric therapy in hospitalized patients with serious infections. Finally, and potentially most disturbingly, the nursing home as a location appears to be a robust reservoir for spread for resistant AB. All of these observations highlight the urgent need to develop novel antibiotics and nontraditional agents, such as antibodies and vaccines, to combat AB infections, in addition to having important infection prevention implications if we are to contain the looming threat of the end of antibiotics.[31]
Disclosure
This study was funded by a grant from Tetraphase Pharmaceuticals, Watertown, MA.
Among hospitalized patients with serious infections, the choice of empiric therapy plays a key role in outcomes.[1, 2, 3, 4, 5, 6, 7, 8, 9] Rising rates and variable patterns of antimicrobial resistance, however, complicate selecting appropriate empiric therapy. Amidst this shifting landscape of resistance to antimicrobials, gram‐negative bacteria and specifically Acinetobacter baumannii (AB), remain a considerable challenge.[10] On the one hand, AB is a less‐frequent cause of serious infections than organisms like Pseudomonas aeruginosa or Enterobacteriaceae in severely ill hospitalized patients.[11, 12] On the other, AB has evolved a variety of resistance mechanisms and exhibits unpredictable susceptibility patterns.[13] These factors combine to increase the likelihood of administering inappropriate empiric therapy when faced with an infection caused by AB and, thereby, raising the risk of death.[14] The fact that clinicians may not routinely consider AB as the potential culprit pathogen in the patient they are treating along with this organism's highly in vitro resistant nature, may result in routine gram‐negative coverage being frequently inadequate for AB infections.
To address the poor outcomes related to inappropriate empiric therapy in the setting of AB, one requires an appreciation of the longitudinal changes and geographic differences in the susceptibility of this pathogen. Thus, we aimed to examine secular trends in the resistance of AB to antimicrobial agents whose effectiveness against this microorganism was well supported in the literature during the study timeframe.[15]
METHODS
To determine the prevalence of predefined resistance patterns among AB in respiratory and blood stream infection (BSI) specimens, we examined The Surveillance Network (TSN) database from Eurofins. We explored data collected between years 2003 and 2012. The database has been used extensively for surveillance purposes since 1994, and has previously been described in detail.[16, 17, 18, 19, 20] Briefly, TSN is a warehouse of routine clinical microbiology data collected from a nationally representative sample of microbiology laboratories in 217 hospitals in the United States. To minimize selection bias, laboratories are included based on their geography and the demographics of the populations they serve.[18] Only clinically significant samples are reported. No personal identifying information for source patients is available in this database. Only source laboratories that perform antimicrobial susceptibility testing according standard Food and Drug Administrationapproved testing methods and that interpret susceptibility in accordance with the Clinical Laboratory Standards Institute breakpoints are included.[21] (See Supporting Table 4 in the online version of this article for minimum inhibitory concentration (MIC) changes over the course of the studycurrent colistin and polymyxin breakpoints applied retrospectively). All enrolled laboratories undergo a pre‐enrollment site visit. Logical filters are used for routine quality control to detect unusual susceptibility profiles and to ensure appropriate testing methods. Repeat testing and reporting are done as necessary.[18]
Laboratory samples are reported as susceptible, intermediate, or resistant. We grouped isolates with intermediate MICs together with the resistant ones for the purposes of the current analysis. Duplicate isolates were excluded. Only samples representing 1 of the 2 infections of interest, respiratory or BSI, were included.
We examined 3 time periods2003 to 2005, 2006 to 2008, and 2009 to 2012for the prevalence of AB's resistance to the following antibiotics: carbapenems (imipenem, meropenem, doripenem), aminoglycosides (tobramycin, amikacin), tetracyclines (minocycline, doxycycline), polymyxins (colistin, polymyxin B), ampicillin‐sulbactam, and trimethoprim‐sulfamethoxazole. Antimicrobial resistance was defined by the designation of intermediate or resistant in the susceptibility category. Resistance to a class of antibiotics was defined as resistance to all drugs within the class for which testing was available. The organism was multidrug resistant (MDR) if it was resistant to at least 1 antimicrobial in at least 3 drug classes examined.[22] Resistance to a combination of 2 drugs was present if the specimen was resistant to both of the drugs in the combination for which testing was available. We examined the data by infection type, time period, the 9 US Census divisions, and location of origin of the sample.
All categorical variables are reported as percentages. Continuous variables are reported as meansstandard deviations and/or medians with the interquartile range (IQR). We did not pursue hypothesis testing due to a high risk of type I error in this large dataset. Therefore, only clinically important trends are highlighted.
RESULTS
Among the 39,320 AB specimens, 81.1% were derived from a respiratory source and 18.9% represented BSI. Demographics of source patients are listed in Table 1. Notably, the median age of those with respiratory infection (58 years; IQR 38, 73) was higher than among patients with BSI (54.5 years; IQR 36, 71), and there were proportionally fewer females among respiratory patients (39.9%) than those with BSI (46.0%). Though only 24.3% of all BSI samples originated from the intensive are unit (ICU), 40.5% of respiratory specimens came from that location. The plurality of all specimens was collected in the 2003 to 2005 time interval (41.3%), followed by 2006 to 2008 (34.7%), with a minority coming from years 2009 to 2012 (24.0%). The proportions of collected specimens from respiratory and BSI sources were similar in all time periods examined (Table 1). Geographically, the South Atlantic division contributed the most samples (24.1%) and East South Central the fewest (2.6%) (Figure 1). The vast majority of all samples came from hospital wards (78.6%), where roughly one‐half originated in the ICU (37.5%). Fewer still came from outpatient sources (18.3%), and a small minority (2.5%) from nursing homes.
| Pneumonia | BSI | All | |
|---|---|---|---|
| |||
| Total, N (%) | 31,868 (81.1) | 7,452 (18.9) | 39,320 |
| Age, y | |||
| Mean (SD) | 57.7 (37.4) | 57.6 (40.6) | 57.7 (38.0) |
| Median (IQR 25, 75) | 58 (38, 73) | 54.5 (36, 71) | 57 (37, 73) |
| Gender, female (%) | 12,725 (39.9) | 3,425 (46.0) | 16,150 (41.1) |
| ICU (%) | 12,9191 (40.5) | 1,809 (24.3) | 14,7284 (37.5) |
| Time period, % total | |||
| 20032005 | 12,910 (40.5) | 3,340 (44.8) | 16,250 (41.3) |
| 20062008 | 11,205 (35.2) | 2,435 (32.7) | 13,640 (34.7) |
| 20092012 | 7,753 (24.3) | 1,677 (22.5) | 9,430 (24.0) |
Figure 2 depicts overall resistance patterns by individual drugs, drug classes, and frequently used combinations of agents. Although doripenem had the highest rate of resistance numerically (90.3%), its susceptibility was tested only in a small minority of specimens (n=31, 0.08%). Resistance to trimethoprim‐sulfamethoxazole was high (55.3%) based on a large number of samples tested (n=33,031). Conversely, colistin as an agent and polymyxins as a class exhibited the highest susceptibility rates of over 90%, though the numbers of samples tested for susceptibility to these drugs were also small (colistin n=2,086, 5.3%; polymyxins n=3,120, 7.9%) (Figure 2). Among commonly used drug combinations, carbapenem+aminoglycoside (18.0%) had the lowest resistance rates, and nearly 30% of all AB specimens tested met the criteria for MDR.
Over time, resistance to carbapenems more‐than doubled, from 21.0% in 2003 to 2005 to 47.9% in 2009 to 2012 (Table 2). Although relatively few samples were tested for colistin susceptibility (n=2,086, 5.3%), resistance to this drug also more than doubled from 2.8% (95% confidence interval: 1.9‐4.2) in 2006 to 2008 to 6.9% (95% confidence interval: 5.7‐8.2) in 2009 to 2012. As a class, however, polymyxins exhibited stable resistance rates over the time frame of the study (Table 2). Prevalence of MDR AB rose from 21.4% in 2003 to 2005 to 33.7% in 2006 to 2008, and remained stable at 35.2% in 2009 to 2012. Resistance to even such broad combinations as carbapenem+ampicillin/sulbactam nearly tripled from 13.2% in 2003 to 2005 to 35.5% in 2009 to 2012. Notably, between 2003 and 2012, although resistance rates either rose or remained stable to all other agents, those to minocycline diminished from 56.5% in 2003 to 2005 to 36.6% in 2006 to 2008 to 30.5% in 2009 to 2012. (See Supporting Table 1 in the online version of this article for time trends based on whether they represented respiratory or BSI specimens, with directionally similar trends in both.)
| Drug/Combination | Time Period | ||||||||
|---|---|---|---|---|---|---|---|---|---|
| 20032005 | 20062008 | 20092012 | |||||||
| Na | %b | 95% CI | N | % | 95% CI | N | % | 95% CI | |
| |||||||||
| Amikacin | 12,949 | 25.2 | 24.5‐26.0 | 10.929 | 35.2 | 34.3‐36.1 | 6,292 | 45.7 | 44.4‐46.9 |
| Tobramycin | 14,549 | 37.1 | 36.3‐37.9 | 11,877 | 41.9 | 41.0‐42.8 | 7,901 | 39.2 | 38.1‐40.3 |
| Aminoglycoside | 14,505 | 22.5 | 21.8‐23.2 | 11,967 | 30.6 | 29.8‐31.4 | 7,736 | 34.8 | 33.8‐35.8 |
| Doxycycline | 173 | 36.4 | 29.6‐43.8 | 38 | 29.0 | 17.0‐44.8 | 32 | 34.4 | 20.4‐51.7 |
| Minocycline | 1,388 | 56.5 | 53.9‐50.1 | 902 | 36.6 | 33.5‐39.8 | 522 | 30.5 | 26.7‐34.5 |
| Tetracycline | 1,511 | 55.4 | 52.9‐57.9 | 940 | 36.3 | 33.3‐39.4 | 546 | 30.8 | 27.0‐34.8 |
| Doripenem | NR | NR | NR | 9 | 77.8 | 45.3‐93.7 | 22 | 95.5 | 78.2‐99.2 |
| Imipenem | 14,728 | 21.8 | 21.2‐22.5 | 12,094 | 40.3 | 39.4‐41.2 | 6,681 | 51.7 | 50.5‐52.9 |
| Meropenem | 7,226 | 37.0 | 35.9‐38.1 | 5,628 | 48.7 | 47.3‐50.0 | 4,919 | 47.3 | 45.9‐48.7 |
| Carbapenem | 15,490 | 21.0 | 20.4‐21.7 | 12,975 | 38.8 | 38.0‐39.7 | 8,778 | 47.9 | 46.9‐49.0 |
| Ampicillin/sulbactam | 10,525 | 35.2 | 34.3‐36.2 | 9,413 | 44.9 | 43.9‐45.9 | 6,460 | 41.2 | 40.0‐42.4 |
| Colistin | NR | NR | NR | 783 | 2.8 | 1.9‐4.2 | 1,303 | 6.9 | 5.7‐8.2 |
| Polymyxin B | 105 | 7.6 | 3.9‐14.3 | 796 | 12.8 | 10.7‐15.3 | 321 | 6.5 | 4.3‐9.6 |
| Polymyxin | 105 | 7.6 | 3.9‐14.3 | 1,563 | 7.9 | 6.6‐9.3 | 1,452 | 6.8 | 5.6‐8.2 |
| Trimethoprim/sulfamethoxazole | 13,640 | 52.5 | 51.7‐53.3 | 11,535 | 57.1 | 56.2‐58.0 | 7,856 | 57.6 | 56.5‐58.7 |
| MDRc | 16,249 | 21.4 | 20.7‐22.0 | 13,640 | 33.7 | 33.0‐34.5 | 9,431 | 35.2 | 34.2‐36.2 |
| Carbapenem+aminoglycoside | 14,601 | 8.9 | 8.5‐9.4 | 12,333 | 21.3 | 20.6‐22.0 | 8,256 | 29.3 | 28.3‐30.3 |
| Aminoglycoside+ampicillin/sulbactam | 10,107 | 12.9 | 12.3‐13.6 | 9,077 | 24.9 | 24.0‐25.8 | 6,200 | 24.3 | 23.2‐25.3 |
| Aminoglycosie+minocycline | 1,359 | 35.6 | 33.1‐38.2 | 856 | 21.4 | 18.8‐24.2 | 503 | 24.5 | 20.9‐28.4 |
| Carbapenem+ampicillin/sulbactam | 10,228 | 13.2 | 12.5‐13.9 | 9,145 | 29.4 | 28.4‐30.3 | 6,143 | 35.5 | 34.3‐36.7 |
Regionally, examining resistance by classes and combinations of antibiotics, trimethoprim‐sulfamethoxazole exhibited consistently the highest rates of resistance, ranging from the lowest in the New England (28.8%) to the highest in the East North Central (69.9%) Census divisions (See Supporting Table 2 in the online version of this article). The rates of resistance to tetracyclines ranged from 0.0% in New England to 52.6% in the Mountain division, and to polymyxins from 0.0% in the East South Central division to 23.4% in New England. Generally, New England enjoyed the lowest rates of resistance (0.0% to tetracyclines to 28.8% to trimethoprim‐sulfamethoxazole), and the Mountain division the highest (0.9% to polymyxins to 52.6% to tetracyclines). The rates of MDR AB ranged from 8.0% in New England to 50.4% in the Mountain division (see Supporting Table 2 in the online version of this article).
Examining resistances to drug classes and combinations by the location of the source specimen revealed that trimethoprim‐sulfamethoxazole once again exhibited the highest rate of resistance across all locations (see Supporting Table 3 in the online version of this article). Despite their modest contribution to the overall sample pool (n=967, 2.5%), organisms from nursing home subjects had the highest prevalence of resistance to aminoglycosides (36.3%), tetracyclines (57.1%), and carbapenems (47.1%). This pattern held true for combination regimens examined. Nursing homes also vastly surpassed other locations in the rates of MDR AB (46.5%). Interestingly, the rates of MDR did not differ substantially among regular inpatient wards (29.2%), the ICU (28.7%), and outpatient locations (26.2%) (see Supporting Table 3 in the online version of this article).
DISCUSSION
In this large multicenter survey we have documented the rising rates of AB resistance to clinically important antimicrobials in the United States. On the whole, all antimicrobials, except for minocycline, exhibited either large or small increases in resistance. Alarmingly, even colistin, a true last resort AB treatment, lost a considerable amount of activity against AB, with the resistance rate rising from 2.8% in 2006 to 2008 to 6.9% in 2009 to 2012. The single encouraging trend that we observed was that resistance to minocycline appeared to diminish substantially, going from over one‐half of all AB tested in 2003 to 2005 to just under one‐third in 2009 to 2012.
Although we did note a rise in the MDR AB, our data suggest a lower percentage of all AB that meets the MDR phenotype criteria compared to reports by other groups. For example, the Center for Disease Dynamics and Economic Policy (CDDEP), analyzing the same data as our study, reports a rise in MDR AB from 32.1% in 1999 to 51.0% in 2010.[23] This discrepancy is easily explained by the fact that we included polymyxins, tetracyclines, and trimethoprim‐sulfamethoxazole in our evaluation, whereas the CDDEP did not examine these agents. Furthermore, we omitted fluoroquinolones, a drug class with high rates of resistance, from our study, because we were interested in focusing only on antimicrobials with clinical data in AB infections.[22] In addition, we limited our evaluation to specimens derived from respiratory or BSI sources, whereas the CDDEP data reflect any AB isolate present in TSN.
We additionally confirm that there is substantial geographic variation in resistance patterns. Thus, despite different definitions, our data agree with those from the CDDEP that the MDR prevalence is highest in the Mountain and East North Central divisions, and lowest in New England overall.[23] The wide variations underscore the fact that it is not valid to speak of national rates of resistance, but rather it is important to concentrate on the local patterns. This information, though important from the macroepidemiologic standpoint, is likely still not granular enough to help clinicians make empiric treatment decisions. In fact, what is needed for that is real‐time antibiogram data specific to each center and even each unit within each center.
The latter point is further illustrated by our analysis of locations of origin of the specimens. In this analysis, we discovered that, contrary to the common presumption that the ICU has the highest rate of resistant organisms, specimens derived from nursing homes represent perhaps the most intensely resistant organisms. In other words, the nursing home is the setting most likely to harbor patients with respiratory infections and BSIs caused by resistant AB. These data are in agreement with several other recent investigations. In a period‐prevalence survey conducted in the state of Maryland in 2009 by Thom and colleagues, long‐term care facilities were found to have the highest prevalence of any AB, and also those resistant to imipenem, MDR, and extensively drug‐resistant organisms.[24] Mortensen and coworkers confirmed the high prevalence of AB and AB resistance in long‐term care facilities, and extended this finding to suggest that there is evidence for intra‐ and interhospital spread of these pathogens.[25] Our data confirm this concerning finding at the national level, and point to a potential area of intervention for infection prevention.
An additional finding of some concern is that the highest proportion of colistin resistance among those specimens, whose location of origin was reported in the database, was the outpatient setting (6.6% compared to 5.4% in the ICU specimens, for example). Although these infections would likely meet the definition for healthcare‐associated infection, AB as a community‐acquired respiratory pathogen is not unprecedented either in the United States or abroad.[26, 27, 28, 29, 30] It is, however, reassuring that most other antimicrobials examined in our study exhibit higher rates of susceptibility in the specimens derived from the outpatient settings than either from the hospital or the nursing home.
Our study has a number of strengths. As a large multicenter survey, it is representative of AB susceptibility patterns across the United States, which makes it highly generalizable. We focused on antibiotics for which clinical evidence is available, thus adding a practical dimension to the results. Another pragmatic consideration is examining the data by geographic distributions, allowing an additional layer of granularity for clinical decisions. At the same time it suffers from some limitations. The TSN database consists of microbiology samples from hospital laboratories. Although we attempted to reduce the risk of duplication, because of how samples are numbered in the database, repeat sampling remains a possibility. Despite having stratified the data by geography and the location of origin of the specimen, it is likely not granular enough for local risk stratification decisions clinicians make daily about the choices of empiric therapy. Some of the MIC breakpoints have changed over the period of the study (see Supporting Table 4 in the online version of this article). Because these changes occurred in the last year of data collection (2012), they should have had only a minimal, if any, impact on the observed rates of resistance in the time frame examined. Additionally, because resistance rates evolve rapidly, more current data are required for effective clinical decision making.
In summary, we have demonstrated that the last decade has seen an alarming increase in the rate of resistance of AB to multiple clinically important antimicrobial agents and classes. We have further emphasized the importance of granularity in susceptibility data to help clinicians make sensible decisions about empiric therapy in hospitalized patients with serious infections. Finally, and potentially most disturbingly, the nursing home as a location appears to be a robust reservoir for spread for resistant AB. All of these observations highlight the urgent need to develop novel antibiotics and nontraditional agents, such as antibodies and vaccines, to combat AB infections, in addition to having important infection prevention implications if we are to contain the looming threat of the end of antibiotics.[31]
Disclosure
This study was funded by a grant from Tetraphase Pharmaceuticals, Watertown, MA.
- National Nosocomial Infections Surveillance (NNIS) System Report. Am J Infect Control. 2004;32:470–485.
- , , , National surveillance of antimicrobial resistance in Pseudomonas aeruginosa isolates obtained from intensive care unit patients from 1993 to 2002. Antimicrob Agents Chemother. 2004;48:4606–4610.
- , , , et al. Health care‐associated pneumonia and community‐acquired pneumonia: a single‐center experience. Antimicrob Agents Chemother. 2007;51:3568–3573.
- , , , et al. Clinical importance of delays in the initiation of appropriate antibiotic treatment for ventilator‐associated pneumonia. Chest. 2002;122:262–268.
- ; ICU‐Acquired Pneumonia Study Group. Modification of empiric antibiotic treatment in patients with pneumonia acquired in the intensive care unit. Intensive Care Med. 1996;22:387–394.
- , , , , Antimicrobial therapy escalation and hospital mortality among patients with HCAP: a single center experience. Chest. 2008:134:963–968.
- , , , et al. Surviving Sepsis Campaign: international guidelines for management of severe sepsis and septic shock: 2008. Crit Care Med. 2008;36:296–327.
- , , , , , Inappropriate antibiotic therapy in Gram‐negative sepsis increases hospital length of stay. Crit Care Med. 2011;39:46–51.
- , , , Inadequate antimicrobial treatment of infections: a risk factor for hospital mortality among critically ill patients. Chest. 1999;115:462–474.
- Centers for Disease Control and Prevention. Antibiotic resistance threats in the United States, 2013. Available at: http://www.cdc.gov/drugresistance/threat-report-2013/pdf/ar-threats-2013-508.pdf#page=59. Accessed December 29, 2014.
- , , , et al.; National Healthcare Safety Network (NHSN) Team and Participating NHSN Facilities. Antimicrobial‐resistant pathogens associated with healthcare‐associated infections: summary of data reported to the National Healthcare Safety Network at the Centers for Disease Control and Prevention, 2009–2010. Infect Control Hosp Epidemiol. 2013;34:1–14.
- , , , , Multi‐drug resistance, inappropriate initial antibiotic therapy and mortality in Gram‐negative severe sepsis and septic shock: a retrospective cohort study. Crit Care. 2014;18(6):596.
- , , , , , Global challenge of multidrug‐resistant Acinetobacter baumannii. Antimicrob Agents Chemother. 2007;51:3471–3484.
- , , , Predictors of hospital mortality among septic ICU patients with Acinetobacter spp. bacteremia: a cohort study. BMC Infect Dis. 2014;14:572.
- , Treatment of Acinetobacter infections. Clin Infect Dis. 2010;51:79–84.
- , , Increasing resistance of Acinetobacter species to imipenem in United States hospitals, 1999–2006. Infect Control Hosp Epidemiol. 2010;31:196–197.
- , , , , Trends in resistance to carbapenems and third‐generation cephalosporins among clinical isolates of Klebsiella pneumoniae in the United States, 1999–2010. Infect Control Hosp Epidemiol. 2013;34:259–268.
- , , Antimicrobial resistance in key bloodstream bacterial isolates: electronic surveillance with the Surveillance Network Database—USA. Clin Infect Dis. 1999;29:259–263.
- , , Community‐associated methicillin‐resistant Staphylococcus aureus in outpatients, United States, 1999–2006. Emerg Infect Dis. 2009;15:1925–1930.
- , , , , Prevalence of antimicrobial resistance in bacteria isolated from central nervous system specimens as reported by U.S. hospital laboratories from 2000 to 2002. Ann Clin Microbiol Antimicrob. 2004;3:3.
- Performance standards for antimicrobial susceptibility testing: twenty‐second informational supplement. CLSI document M100‐S22. Wayne, PA: Clinical and Laboratory Standards Institute; 2012.
- , , , et al. Multidrug‐resistant, extensively drug‐resistant and pandrug‐resistant bacteria: an international expert proposal for interim standard definitions for acquired resistance. Clin Microbiol Infect. 2012;18:268–281.
- CDDEP: The Center for Disease Dynamics, Economics and Policy. Resistance map: Acinetobacter baumannii overview. Available at: http://www.cddep.org/projects/resistance_map/acinetobacter_baumannii_overview. Accessed January 16, 2015.
- , , , et al.; Maryland MDRO Prevention Collaborative. Assessing the burden of Acinetobacter baumannii in Maryland: a statewide cross‐sectional period prevalence survey. Infect Control Hosp Epidemiol. 2012;33:883–888.
- , , , et al. Multidrug‐resistant Acinetobacter baumannii infection, colonization, and transmission related to a long‐term care facility providing subacute care. Infect Control Hosp Epidemiol. 2014;35:406–411.
- , , , , , Severe community‐acquired pneumonia due to Acinetobacter baumannii. Chest. 2001;120:1072–1077.
- , , , , , Fulminant community‐acquired Acinetobacter baumannii pneumonia as distinct clinical syndrome. Chest. 2006;129:102–109.
- , , , Community‐acquired Acinetobacter baumannii pneumonia. Rev Clin Esp. 2003;203:284–286.
- , , , et al. Antimicrobial drug‐resistant microbes associated with hospitalized community‐acquired and healthcare‐associated pneumonia: a multi‐center study in Taiwan. J Formos Med Assoc. 2013;112:31–40.
- , , , , Antimicrobial resistance in Hispanic patients hospitalized in San Antonio, TX with community‐acquired pneumonia. Hosp Pract (1995). 2010;38:108–113.
- Centers for Disease Control and Prevention. CDC director blog. The end of antibiotics. Can we come back from the brink? Available at: http://blogs.cdc.gov/cdcdirector/2014/05/05/the-end-of-antibiotics-can-we-come-back-from-the-brink/. Published May 5, 2014. Accessed January 16, 2015.
- National Nosocomial Infections Surveillance (NNIS) System Report. Am J Infect Control. 2004;32:470–485.
- , , , National surveillance of antimicrobial resistance in Pseudomonas aeruginosa isolates obtained from intensive care unit patients from 1993 to 2002. Antimicrob Agents Chemother. 2004;48:4606–4610.
- , , , et al. Health care‐associated pneumonia and community‐acquired pneumonia: a single‐center experience. Antimicrob Agents Chemother. 2007;51:3568–3573.
- , , , et al. Clinical importance of delays in the initiation of appropriate antibiotic treatment for ventilator‐associated pneumonia. Chest. 2002;122:262–268.
- ; ICU‐Acquired Pneumonia Study Group. Modification of empiric antibiotic treatment in patients with pneumonia acquired in the intensive care unit. Intensive Care Med. 1996;22:387–394.
- , , , , Antimicrobial therapy escalation and hospital mortality among patients with HCAP: a single center experience. Chest. 2008:134:963–968.
- , , , et al. Surviving Sepsis Campaign: international guidelines for management of severe sepsis and septic shock: 2008. Crit Care Med. 2008;36:296–327.
- , , , , , Inappropriate antibiotic therapy in Gram‐negative sepsis increases hospital length of stay. Crit Care Med. 2011;39:46–51.
- , , , Inadequate antimicrobial treatment of infections: a risk factor for hospital mortality among critically ill patients. Chest. 1999;115:462–474.
- Centers for Disease Control and Prevention. Antibiotic resistance threats in the United States, 2013. Available at: http://www.cdc.gov/drugresistance/threat-report-2013/pdf/ar-threats-2013-508.pdf#page=59. Accessed December 29, 2014.
- , , , et al.; National Healthcare Safety Network (NHSN) Team and Participating NHSN Facilities. Antimicrobial‐resistant pathogens associated with healthcare‐associated infections: summary of data reported to the National Healthcare Safety Network at the Centers for Disease Control and Prevention, 2009–2010. Infect Control Hosp Epidemiol. 2013;34:1–14.
- , , , , Multi‐drug resistance, inappropriate initial antibiotic therapy and mortality in Gram‐negative severe sepsis and septic shock: a retrospective cohort study. Crit Care. 2014;18(6):596.
- , , , , , Global challenge of multidrug‐resistant Acinetobacter baumannii. Antimicrob Agents Chemother. 2007;51:3471–3484.
- , , , Predictors of hospital mortality among septic ICU patients with Acinetobacter spp. bacteremia: a cohort study. BMC Infect Dis. 2014;14:572.
- , Treatment of Acinetobacter infections. Clin Infect Dis. 2010;51:79–84.
- , , Increasing resistance of Acinetobacter species to imipenem in United States hospitals, 1999–2006. Infect Control Hosp Epidemiol. 2010;31:196–197.
- , , , , Trends in resistance to carbapenems and third‐generation cephalosporins among clinical isolates of Klebsiella pneumoniae in the United States, 1999–2010. Infect Control Hosp Epidemiol. 2013;34:259–268.
- , , Antimicrobial resistance in key bloodstream bacterial isolates: electronic surveillance with the Surveillance Network Database—USA. Clin Infect Dis. 1999;29:259–263.
- , , Community‐associated methicillin‐resistant Staphylococcus aureus in outpatients, United States, 1999–2006. Emerg Infect Dis. 2009;15:1925–1930.
- , , , , Prevalence of antimicrobial resistance in bacteria isolated from central nervous system specimens as reported by U.S. hospital laboratories from 2000 to 2002. Ann Clin Microbiol Antimicrob. 2004;3:3.
- Performance standards for antimicrobial susceptibility testing: twenty‐second informational supplement. CLSI document M100‐S22. Wayne, PA: Clinical and Laboratory Standards Institute; 2012.
- , , , et al. Multidrug‐resistant, extensively drug‐resistant and pandrug‐resistant bacteria: an international expert proposal for interim standard definitions for acquired resistance. Clin Microbiol Infect. 2012;18:268–281.
- CDDEP: The Center for Disease Dynamics, Economics and Policy. Resistance map: Acinetobacter baumannii overview. Available at: http://www.cddep.org/projects/resistance_map/acinetobacter_baumannii_overview. Accessed January 16, 2015.
- , , , et al.; Maryland MDRO Prevention Collaborative. Assessing the burden of Acinetobacter baumannii in Maryland: a statewide cross‐sectional period prevalence survey. Infect Control Hosp Epidemiol. 2012;33:883–888.
- , , , et al. Multidrug‐resistant Acinetobacter baumannii infection, colonization, and transmission related to a long‐term care facility providing subacute care. Infect Control Hosp Epidemiol. 2014;35:406–411.
- , , , , , Severe community‐acquired pneumonia due to Acinetobacter baumannii. Chest. 2001;120:1072–1077.
- , , , , , Fulminant community‐acquired Acinetobacter baumannii pneumonia as distinct clinical syndrome. Chest. 2006;129:102–109.
- , , , Community‐acquired Acinetobacter baumannii pneumonia. Rev Clin Esp. 2003;203:284–286.
- , , , et al. Antimicrobial drug‐resistant microbes associated with hospitalized community‐acquired and healthcare‐associated pneumonia: a multi‐center study in Taiwan. J Formos Med Assoc. 2013;112:31–40.
- , , , , Antimicrobial resistance in Hispanic patients hospitalized in San Antonio, TX with community‐acquired pneumonia. Hosp Pract (1995). 2010;38:108–113.
- Centers for Disease Control and Prevention. CDC director blog. The end of antibiotics. Can we come back from the brink? Available at: http://blogs.cdc.gov/cdcdirector/2014/05/05/the-end-of-antibiotics-can-we-come-back-from-the-brink/. Published May 5, 2014. Accessed January 16, 2015.
© 2015 Society of Hospital Medicine
Sepsis and Septic Shock Readmission Risk
Despite its decreasing mortality, sepsis remains a leading reason for intensive care unit (ICU) admission and is associated with crude mortality in excess of 25%.[1, 2] In the United States there are between 660,000 and 750,000 sepsis hospitalizations annually, with the direct costs surpassing $24 billion.[3, 4, 5] As mortality rates have begun to fall, attention has shifted to issues of morbidity and recovery, the intermediate and longer‐term consequences associated with survivorship, and how interventions made while the patient is acutely ill in the ICU alter later health outcomes.[3, 5, 6, 7, 8]
One area of particular interest is the need for healthcare utilization following an acute admission for sepsis, and specifically rehospitalization within 30 days of discharge. This outcome is important not just from the perspective of the patient's well‐being, but also from the point of view of healthcare financing. Through the establishment of Hospital Readmission Reduction Program, the Centers for Medicare and Medicaid Services have sharply reduced reimbursement to hospitals for excessive rates of 30‐day readmissions.[9]
For sepsis, little is known about such readmissions, and even less about how to prevent them. A handful of studies suggest that this rate is between 5% and 26%.[10, 11, 12, 13] Whereas some of these studies looked at some of the factors that impact readmissions,[11, 12] none examined the potential contribution of microbiology of sepsis to this outcome.
To explore these questions, we conducted a single‐center retrospective cohort study among critically ill patients admitted to the ICU with severe culture‐positive sepsis and/or septic shock and determined the rate of early posthospital discharge readmission. In addition, we sought to elucidate predictors of subsequent readmission.
METHODS
Study Design and Ethical Standards
We conducted a single‐center retrospective cohort study from January 2008 to December 2012. The study was approved by the Washington University School of Medicine Human Studies Committee, and informed consent was waived because the data collection was retrospective without any patient‐identifying information. The study was performed in accordance with the ethical standards of the 1964 Declaration of Helsinki and its later amendments. Aspects of our methodology have been previously published.[14]
Primary Endpoint
All‐cause readmission to an acute‐care facility in the 30 days following discharge after the index hospitalization with sepsis served as the primary endpoint. The index hospitalizations occurred at the Barnes‐Jewish Hospital, a 1200‐bed inner‐city academic institution that serves as the main teaching institution for BJC HealthCare, a large integrated healthcare system of both inpatient and outpatient care. BJC includes a total of 13 hospitals in a compact geographic region surrounding and including St. Louis, Missouri, and we included readmission to any of these hospitals in our analysis. Persons treated within this healthcare system are, in nearly all cases, readmitted to 1 of the system's participating 13 hospitals. If a patient who receives healthcare in the system presents to an out‐of‐system hospital, he/she is often transferred back into the integrated system because of issues of insurance coverage.
Study Cohort
All consecutive adult ICU patients were included if (1) They had a positive blood culture for a pathogen (Cultures positive only for coagulase negative Staphylococcus aureus were excluded as contaminants.), (2) there was an International Classification of Diseases, Ninth Revision, Clinical Modification (ICD‐9‐CM) code corresponding to an acute organ dysfunction,[4] and (3) they survived their index hospitalization. Only the first episode of sepsis was included as the index hospitalization.
Definitions
All‐cause 30‐day readmission, was defined as a repeat hospitalization within 30 days of discharge from the index hospitalization among survivors of culture‐positive severe sepsis or septic shock. The definition of severe sepsis was based on discharge ICD‐9‐CM codes for acute organ dysfunction.[3] Patients were classified as having septic shock if vasopressors (norepinephrine, dopamine, epinephrine, phenylephrine, or vasopressin) were initiated within 24 hours of the blood culture collection date and time.
Initially appropriate antimicrobial treatment (IAAT) was deemed appropriate if the initially prescribed antibiotic regimen was active against the identified pathogen based on in vitro susceptibility testing and administered for at least 24 hours within 24 hours following blood culture collection. All other regimens were classified as non‐IAAT. Combination antimicrobial treatment was not required for IAAT designation.[15] Prior antibiotic exposure and prior hospitalization occurred within the preceding 90 days, and prior bacteremia within 30 days of the index episode. Multidrug resistance (MDR) among Gram‐negative bacteria was defined as nonsusceptibility to at least 1 antimicrobial agent from at least 3 different antimicrobial classes.[16] Both extended spectrum ‐lactamase (ESBL) organisms and carbapenemase‐producing Enterobacteriaceae were identified via molecular testing.
Healthcare‐associated (HCA) infections were defined by the presence of at least 1 of the following: (1) recent hospitalization, (2) immune suppression (defined as any primary immune deficiency or acquired immune deficiency syndrome or exposure within 3 prior months to immunosuppressive treatmentschemotherapy, radiation therapy, or steroids), (3) nursing home residence, (4) hemodialysis, (5) prior antibiotics. and (6) index bacteremia deemed a hospital‐acquired bloodstream infection (occurring >2 days following index admission date). Acute kidney injury (AKI) was defined according to the RIFLE (Risk, Injury, Failure, Loss, End‐stage) criteria based on the greatest change in serum creatinine (SCr).[17]
Data Elements
Patient‐specific baseline characteristics and process of care variables were collected from the automated hospital medical record, microbiology database, and pharmacy database of Barnes‐Jewish Hospital. Electronic inpatient and outpatient medical records available for all patients in the BJC HealthCare system were reviewed to determine prior antibiotic exposure. The baseline characteristics collected during the index hospitalization included demographics and comorbid conditions. The comorbidities were identified based on their corresponding ICD‐9‐CM codes. The Acute Physiology and Chronic Health Evaluation (APACHE) II and Charlson comorbidity scores were calculated based on clinical data present during the 24 hours after the positive blood cultures were obtained.[18] This was done to accommodate patients with community‐acquired and healthcare‐associated community‐onset infections who only had clinical data available after blood cultures were drawn. Lowest and highest SCr levels were collected during the index hospitalization to determine each patient's AKI status.
Statistical Analyses
Continuous variables were reported as means with standard deviations and as medians with 25th and 75th percentiles. Differences between mean values were tested via the Student t test, and between medians using the Mann‐Whitney U test. Categorical data were summarized as proportions, and the 2 test or Fisher exact test for small samples was used to examine differences between groups. We developed multiple logistic regression models to identify clinical risk factors that were associated with 30‐day all‐cause readmission. All risk factors that were significant at 0.20 in the univariate analyses, as well as all biologically plausible factors even if they did not reach this level of significance, were included in the models. All variables entered into the models were assessed for collinearity, and interaction terms were tested. The most parsimonious models were derived using the backward manual elimination method, and the best‐fitting model was chosen based on the area under the receiver operating characteristics curve (AUROC or the C statistic). The model's calibration was assessed with the Hosmer‐Lemeshow goodness‐of‐fit test. All tests were 2‐tailed, and a P value <0.05 represented statistical significance.
All computations were performed in Stata/SE, version 9 (StataCorp, College Station, TX).
Role of Sponsor
The sponsor had no role in the design, analyses, interpretation, or publication of the study.
RESULTS
Among the 1697 patients with severe sepsis or septic shock who were discharged alive from the hospital, 543 (32.0%) required a rehospitalization within 30 days. There were no differences in age or gender distribution between the groups (Table 1). All comorbidities examined were more prevalent among those with a 30‐day readmission than among those without, with the median Charlson comorbidity score reflecting this imbalance (5 vs 4, P<0.001). Similarly, most of the HCA risk factors were more prevalent among the readmitted group than the comparator group, with HCA sepsis among 94.2% of the former and 90.7% of the latter (P = 0.014).
| 30‐Day Readmission = Yes | 30‐Day Readmission = No | ||||
|---|---|---|---|---|---|
| N = 543 | % = 32.00% | N = 1,154 | % = 68.00% | P Value | |
| |||||
| Baseline characteristics | |||||
| Age, y | |||||
| Mean SD | 58.5 15.7 | 59.5 15.8 | |||
| Median (25, 75) | 60 (49, 69) | 60 (50, 70) | 0.297 | ||
| Race | |||||
| Caucasian | 335 | 61.69% | 769 | 66.64% | 0.046 |
| African American | 157 | 28.91% | 305 | 26.43% | 0.284 |
| Other | 9 | 1.66% | 22 | 1.91% | 0.721 |
| Sex, female | 244 | 44.94% | 537 | 46.53% | 0.538 |
| Admission source | |||||
| Home | 374 | 68.88% | 726 | 62.91% | 0.016 |
| Nursing home, rehab, or LTAC | 39 | 7.81% | 104 | 9.01% | 0.206 |
| Transfer from another hospital | 117 | 21.55% | 297 | 25.74% | 0.061 |
| Comorbidities | |||||
| CHF | 131 | 24.13% | 227 | 19.67% | 0.036 |
| COPD | 156 | 28.73% | 253 | 21.92% | 0.002 |
| CLD | 83 | 15.29% | 144 | 12.48% | 0.113 |
| DM | 175 | 32.23% | 296 | 25.65% | 0.005 |
| CKD | 137 | 25.23% | 199 | 17.24% | <0.001 |
| Malignancy | 225 | 41.44% | 395 | 34.23% | 0.004 |
| HIV | 11 | 2.03% | 10 | 0.87% | 0.044 |
| Charlson comorbidity score | |||||
| Mean SD | 5.24 3.32 | 4.48 3.35 | |||
| Median (25, 75) | 5 (3, 8) | 4 (2, 7) | <0.001 | ||
| HCA RF | 503 | 94.19% | 1,019 | 90.66% | 0.014 |
| Hemodialysis | 65 | 12.01% | 114 | 9.92% | 0.192 |
| Immune suppression | 193 | 36.07% | 352 | 31.21% | 0.044 |
| Prior hospitalization | 339 | 65.07% | 620 | 57.09% | 0.002 |
| Nursing home residence | 39 | 7.81% | 104 | 9.01% | 0.206 |
| Prior antibiotics | 301 | 55.43% | 568 | 49.22% | 0.017 |
| Hospital‐acquired BSI* | 240 | 44.20% | 485 | 42.03% | 0.399 |
| Prior bacteremia within 30 days | 88 | 16.21% | 154 | 13.34% | 0.116 |
| Sepsis‐related parameters | |||||
| LOS prior to bacteremia, d | |||||
| Mean SD | 6.65 11.22 | 5.88 10.81 | |||
| Median (25, 75) | 1 (0, 10) | 0 (0, 8) | 0.250 | ||
| Surgery | |||||
| None | 362 | 66.67% | 836 | 72.44% | 0.015 |
| Abdominal | 104 | 19.15% | 167 | 14.47% | 0.014 |
| Extra‐abdominal | 73 | 13.44% | 135 | 11.70% | 0.306 |
| Status unknown | 4 | 0.74% | 16 | 1.39% | 0.247 |
| Central line | 333 | 64.41% | 637 | 57.80% | 0.011 |
| TPN at the time of bacteremia or prior to it during index hospitalization | 52 | 9.74% | 74 | 5.45% | 0.017 |
| APACHE II | |||||
| Mean SD | 15.08 5.47 | 15.35 5.43 | |||
| Median (25, 75) | 15 (11, 18) | 15 (12, 19) | 0.275 | ||
| Severe sepsis | 361 | 66.48% | 747 | 64.73% | 0.480 |
| Septic shock requiring vasopressors | 182 | 33.52% | 407 | 35.27% | |
| On MV | 104 | 19.22% | 251 | 21.90% | 0.208 |
| Peak WBC (103/L) | |||||
| Mean SD | 22.26 25.20 | 22.14 17.99 | |||
| Median (25, 75) | 17.1 (8.9, 30.6) | 16.9 (10, 31) | 0.654 | ||
| Lowest serum SCr, mg/dL | |||||
| Mean SD | 1.02 1.05 | 0.96 1.03 | |||
| Median (25, 75) | 0.68 (0.5, 1.06) | 0.66 (0.49, 0.96) | 0.006 | ||
| Highest serum SCr, mg/dL | |||||
| Mean SD | 2.81 2.79 | 2.46 2.67 | |||
| Median (25, 75) | 1.68 (1.04, 3.3) | 1.41 (0.94, 2.61) | 0.001 | ||
| RIFLE category | |||||
| None | 81 | 14.92% | 213 | 18.46% | 0.073 |
| Risk | 112 | 20.63% | 306 | 26.52% | 0.009 |
| Injury | 133 | 24.49% | 247 | 21.40% | 0.154 |
| Failure | 120 | 22.10% | 212 | 18.37% | 0.071 |
| Loss | 50 | 9.21% | 91 | 7.89% | 0.357 |
| End‐stage | 47 | 8.66% | 85 | 7.37% | 0.355 |
| Infection source | |||||
| Urine | 95 | 17.50% | 258 | 22.36% | 0.021 |
| Abdomen | 69 | 12.71% | 113 | 9.79% | 0.070 |
| Lung | 93 | 17.13% | 232 | 20.10% | 0.146 |
| Line | 91 | 16.76% | 150 | 13.00% | 0.038 |
| CNS | 1 | 0.18% | 16 | 1.39% | 0.012 |
| Skin | 51 | 9.39% | 82 | 7.11% | 0.102 |
| Unknown | 173 | 31.86% | 375 | 32.50% | 0.794 |
During the index hospitalization, 589 patients (34.7%) suffered from septic shock requiring vasopressors; this did not impact the 30‐day readmission risk (Table 1). Commensurately, markers of severity of acute illness (APACHE II score, mechanical ventilation, peak white blood cell count) did not differ between the groups. With respect to the primary source of sepsis, urine was less, whereas central nervous system was more likely among those readmitted within 30 days. Similarly, there was a significant imbalance between the groups in the prevalence of AKI (Table 1). Specifically, those who did require a readmission were slightly less likely to have sustained no AKI (RIFLE: None; 14.9% vs 18.5%, P = 0.073). Those requiring readmission were also less likely to be in the category RIFLE: Risk (20.6% vs 26.5%, P = 0.009). The direction of this disparity was reversed for the Injury and Failure categories. No differences between groups were seen among those with categories Loss and end‐stage kidney disease (ESKD) (Table 1).
The microbiology of sepsis did not differ in most respects between the 30‐day readmission groups, save for several organisms (Table 2). Most strikingly, those who required a readmission were more likely than those who did not to be infected with Bacteroides spp, Candida spp, an MDR or an ESBL organism (Table 2). As for the outcomes of the index hospitalization, those with a repeat admission had a longer overall and postonset of sepsis initial hospital length of stay, and were less likely to be discharged either home without home health care or transferred to another hospital at the end of their index hospitalization (Table 3).
| 30‐Day Readmission = Yes | 30‐Day Readmission = No | P Value | |||
|---|---|---|---|---|---|
| N | % | N | % | ||
| |||||
| 543 | 32.00% | 1,154 | 68.00% | ||
| Gram‐positive BSI | 260 | 47.88% | 580 | 50.26% | 0.376 |
| Staphylococcus aureus | 138 | 25.41% | 287 | 24.87% | 0.810 |
| MRSA | 78 | 14.36% | 147 | 12.74% | 0.358 |
| VISA | 6 | 1.10% | 9 | 0.78% | 0.580 |
| Streptococcus pneumoniae | 7 | 1.29% | 33 | 2.86% | 0.058 |
| Streptococcus spp | 34 | 6.26% | 81 | 7.02% | 0.606 |
| Peptostreptococcus spp | 5 | 0.92% | 15 | 1.30% | 0.633 |
| Clostridium perfringens | 4 | 0.74% | 10 | 0.87% | 1.000 |
| Enterococcus faecalis | 54 | 9.94% | 108 | 9.36% | 0.732 |
| Enterococcus faecium | 29 | 5.34% | 63 | 5.46% | 1.000 |
| VRE | 36 | 6.63% | 70 | 6.07% | 0.668 |
| Gram‐negative BSI | 231 | 42.54% | 515 | 44.63% | 0.419 |
| Escherichia coli | 54 | 9.94% | 151 | 13.08% | 0.067 |
| Klebsiella pneumoniae | 54 | 9.94% | 108 | 9.36% | 0.723 |
| Klebsiella oxytoca | 11 | 2.03% | 18 | 1.56% | 0.548 |
| Enterobacter aerogenes | 6 | 1.10% | 13 | 1.13% | 1.000 |
| Enterobacter cloacae | 21 | 3.87% | 44 | 3.81% | 1.000 |
| Pseudomonas aeruginosa | 28 | 5.16% | 65 | 5.63% | 0.733 |
| Acinetobacter spp | 8 | 1.47% | 27 | 2.34% | 0.276 |
| Bacteroides spp | 25 | 4.60% | 30 | 2.60% | 0.039 |
| Serratia marcescens | 14 | 2.58% | 21 | 1.82% | 0.360 |
| Stenotrophomonas maltophilia | 3 | 0.55% | 8 | 0.69% | 1.000 |
| Achromobacter spp | 2 | 0.37% | 3 | 0.17% | 0.597 |
| Aeromonas spp | 2 | 0.37% | 1 | 0.09% | 0.241 |
| Burkholderia cepacia | 0 | 0.00% | 6 | 0.52% | 0.186 |
| Citrobacter freundii | 2 | 0.37% | 15 | 1.39% | 0.073 |
| Fusobacterium spp | 7 | 1.29% | 10 | 0.87% | 0.438 |
| Haemophilus influenzae | 1 | 0.18% | 4 | 0.35% | 1.000 |
| Prevotella spp | 1 | 0.18% | 6 | 0.52% | 0.441 |
| Proteus mirabilis | 9 | 1.66% | 39 | 3.38% | 0.058 |
| MDR PA | 2 | 0.37% | 7 | 0.61% | 0.727 |
| ESBL | 10 | 6.25% | 8 | 2.06% | 0.017 |
| CRE | 2 | 1.25% | 0 | 0.00% | 0.028 |
| MDR Gram‐negative or Gram‐positive | 231 | 47.53% | 450 | 41.86% | 0.036 |
| Candida spp | 58 | 10.68% | 76 | 6.59% | 0.004 |
| Polymicrobal BSI | 50 | 9.21% | 111 | 9.62% | 0.788 |
| Initially inappropriate treatment | 119 | 21.92% | 207 | 17.94% | 0.052 |
| 30‐Day Readmission = Yes | 30‐Day Readmission = No | ||||
|---|---|---|---|---|---|
| N = 543 | % = 32.00% | N = 1,154 | % = 68.00% | P Value | |
| |||||
| Hospital LOS, days | |||||
| Mean SD | 26.44 23.27 | 23.58 21.79 | 0.019 | ||
| Median (25, 75) | 19.16 (9.66, 35.86) | 17.77 (8.9, 30.69) | |||
| Hospital LOS following BSI onset, days | |||||
| Mean SD | 19.80 18.54 | 17.69 17.08 | 0.022 | ||
| Median (25, 75) | 13.9 (7.9, 25.39) | 12.66 (7.05, 22.66) | |||
| Discharge destination | |||||
| Home | 125 | 23.02% | 334 | 28.94% | 0.010 |
| Home with home care | 163 | 30.02% | 303 | 26.26% | 0.105 |
| Rehab | 81 | 14.92% | 149 | 12.91% | 0.260 |
| LTAC | 41 | 7.55% | 87 | 7.54% | 0.993 |
| Transfer to another hospital | 1 | 0.18% | 19 | 1.65% | 0.007 |
| SNF | 132 | 24.31% | 262 | 22.70% | 0.465 |
In a logistic regression model, 5 factors emerged as predictors of 30‐day readmission (Table 4). Having RIFLE: Injury or RIFLE: Failure carried an approximately 2‐fold increase in the odds of 30‐day rehospitalization (odds ratio: 1.95, 95% confidence interval: 1.302.93, P = 0.001) relative to having a RIFLE: None or RIFLE: Risk. Although having strong association with this outcome, harboring an ESBL organism or Bacteroides spp were both relatively infrequent events (3.3% ESBL and 3.2% Bacteroides spp). Infection with Escherichia coli and urine as the source of sepsis both appeared to be significantly protective against a readmission (Table 4). The model's discrimination was moderate (AUROC = 0.653) and its calibration adequate (Hosmer‐Lemeshow P = 0.907). (See Supporting Information, Appendix 1, in the online version of this article for the steps in the development of the final model.)
| OR | 95% CI | P Value | |
|---|---|---|---|
| |||
| ESBL | 4.503 | 1.42914.190 | 0.010 |
| RIFLE: Injury or Failure (reference: RIFLE: None or Risk) | 1.951 | 1.2972.933 | 0.001 |
| Bacteroides spp | 2.044 | 1.0583.948 | 0.033 |
| Source: urine | 0.583 | 0.3470.979 | 0.041 |
| Escherichia coli | 0.494 | 0.2700.904 | 0.022 |
DISCUSSION
In this single‐center retrospective cohort study, nearly one‐third of survivors of culture‐positive severe sepsis or septic shock required a rehospitalization within 30 days of discharge from their index admission. Factors that contributed to a higher odds of rehospitalization were having mild‐to‐moderate AKI (RIFLE: Injury or RIFLE: Failure) and infection with ESBL organisms or Bacteroides spp, whereas urine as the source of sepsis and E coli as the pathogen appeared to be protective.
A recent study by Hua and colleagues examining the New York Statewide Planning and Research Cooperative System for the years 2008 to 2010 noted a 16.2% overall rate of 30‐day rehospitalization among survivors of initial critical illness.[11] Just as we observed, Hua et al. concluded that development of AKI correlated with readmission. Because they relied on administrative data for their analysis, AKI was diagnosed when hemodialysis was utilized. Examining AKI using SCr changes, our findings add a layer of granularity to the relationship between AKI stages and early readmission. Specifically, we failed to detect any rise in the odds of rehospitalization when either very mild (RIFLE: Risk) or severe (RIFLE: Loss or RIFLE: ESKD) AKI was present. Only when either RIFLE: Injury or RIFLE: Failure developed did the odds of readmission rise. In addition to diverging definitions between our studies, differences in populations also likely yielded different results.[11] Although Hua et al. examined all admissions to the ICU regardless of the diagnosis or illness severity, our cohort consisted of only those ICU patients who survived culture‐positive severe sepsis/septic shock. Because AKI is a known risk factor for mortality in sepsis,[19] the potential for immortal time bias leaves a smaller pool of surviving patients with ESKD at risk for readmission. Regardless of the explanation, it may be prudent to focus on preventing AKI not only to improve survival, but also from the standpoint of diminishing the risk of an early readmission.
Four additional studies have examined the frequency of early readmissions among survivors of critical illness. Liu et al. noted 17.9% 30‐day rehospitalization rate among sepsis survivors.[12] Factors associated with the risk of early readmission included acute and chronic diseases burdens, index hospital LOS, and the need for the ICU in the index sepsis admission. In contrast to our cohort, all of whom were in the ICU during their index episode, less than two‐thirds of the entire population studied by Liu had required an ICU admission. Additionally, Liu's study did not specifically examine the potential impact of AKI or of microbiology on this outcome.
Prescott and coworkers examined healthcare utilization following an episode of severe sepsis.[13] Among other findings, they reported a 30‐day readmission rate of 26.5% among survivors. Although closer to our estimate, this study included all patients surviving a severe sepsis hospitalization, and not only those with a positive culture. These investigators did not examine predictors of readmission.[13]
Horkan et al. examined specifically whether there was an association between AKI and postdischarge outcomes, including 30‐day readmission risk, in a large cohort of patients who survived their critical illness.[20] In it they found that readmission risk ranged from 19% to 21%, depending on the extent of the AKI. Moreover, similar to our findings, they reported that in an adjusted analysis RIFLE: Injury and RIFLE: Failure were associated with a rise in the odds of a 30‐day rehospitalizaiton. In contrast to our study, Horkan et al. did detect an increase in the odds of this outcome associated with RIFLE: Risk. There are likely at least 3 reasons for this difference. First, we focused only on patients with severe sepsis or septic shock, whereas Horkan and colleagues included all critical illness survivors. Second, we were able to explore the impact of microbiology on this outcome. Third, Horkan's study included an order of magnitude more patients than did ours, thus making it more likely either to have the power to detect a true association that we may have lacked or to be more susceptible to type I error.
Finally, Goodwin and colleagues utilized 3 states' databases included in the Health Care and Utilization Project (HCUP) from the Agency for Healthcare Research and Quality to study frequency and risk factors for 30‐day readmission among survivors of severe sepsis.[21] Patients were identified based on the use of the severe sepsis (995.92) and septic shock (785.52). These authors found a 30‐day readmission rate of 26%. Although chronic renal disease, among several other factors, was associated with an increase in this risk, the data source did not permit these investigators to examine the impact of AKI on the outcomes. Similarly, HCUP data do not contain microbiology, a distinct difference from our analysis.
If clinicians are to pursue strategies to reduce the risk of an all‐cause 30‐day readmission, the key goal is not simply to identify all variables associated with readmission, but to focus on factors that are potentially modifiable. Although neither Hua nor Liu and their teams identified any additional factors that are potentially modifiable,[11, 12] in the present study, among the 5 factors we identified, the development of mild to moderate AKI during the index hospitalization may deserve stronger consideration for efforts at prevention. Although one cannot conclude automatically that preventing AKI in this population could mitigate some of the early rehospitalization risk, critically ill patients are frequently exposed to a multitude of nephrotoxic agents. Those caring for subjects with sepsis should reevaluate the risk‐benefit equation of these factors more cautiously and apply guideline‐recommended AKI prevention strategies more aggressively, particularly because a relatively minor change in SCr resulted in an excess risk of readmission.[22]
In addition to AKI, which is potentially modifiable, we identified several other clinical factors predictive of 30‐day readmission, which are admittedly not preventable. Thus, microbiology was predictive of this outcome, with E coli engendering fewer and Bacteroides spp and ESBL organisms more early rehospitalizations. Similarly, urine as the source of sepsis was associated with a lower risk for this endpoint.
Our study has a number of limitations. As a retrospective cohort, it is subject to bias, most notably a selection bias. Specifically, because the flagship hospital of the BJC HealthCare system is a referral center, it is possible that we did not capture all readmissions. However, generally, if a patient who receives healthcare within 1 of the BJC hospitals presents to a nonsystem hospital, that patient is nearly always transferred back into the integrated system because of issues of insurance coverage. Analysis of certain diagnosis‐related groups has indicated that 73% of all patients overall discharged from 4 of the large BJC system institutions who require a readmission within 30 days of discharge return to a BJC hospital (personal communication, Financial Analysis and Decision Support Department at BJC to Dr. Kollef May 12, 2015). Therefore, we may have misclassified the outcome in as many as 180 patients. The fact that our readmission rate was fully double that seen in Hua et al.'s and Liu et al.'s studies, and somewhat higher than that reported by Prescott et al., attests not only to the population differences, but also to the fact that we are unlikely to have missed a substantial percentage of readmissions.[11, 12, 13] Furthermore, to mitigate biases, we enrolled all consecutive patients meeting the predetermined criteria. Missing from our analysis are events that occurred between the index discharge and the readmission. Likewise, we were unable to obtain such potentially important variables as code status or outpatient mortality following discharge. These intervening factors, if included in subsequent studies, may increase the predictive power of the model. Because we relied on administrative coding to identify cases of severe sepsis and septic shock, it is possible that there is misclassification within our cohort. Recent studies indicate, however, that the Angus definition, used in our study, has high negative and positive predictive values for severe sepsis identification.[23] It is still possible that our cohort is skewed toward a more severely ill population, making our results less generalizable to the less severely ill septic patients.[24] The study was performed at a single healthcare system and included only cases of severe sepsis or septic shock that had a positive blood culture, and thus the findings may not be broadly generalizable either to patients without a positive blood culture or to institutions that do not resemble it.
In summary, we have demonstrated that survivors of culture‐positive severe sepsis or septic shock have a high rate of 30‐day rehospitalization. Because the US federal government's initiatives deem 30‐day readmissions to be a quality metric and penalize institutions with higher‐than average readmission rates, a high volume of critically ill patients with culture‐positive severe sepsis and septic shock may disproportionately put an institution at risk for such penalties. Unfortunately, not many of the determinants of readmission are amenable to prevention. As sepsis survival continues to improve, hospitals will need to concentrate their resources on coordinating care of these complex patients so as to improve both individual quality of life and the quality of care that they provide.
Disclosures
This study was supported by a research grant from Cubist Pharmaceuticals, Lexington, Massachusetts. Dr. Kollef's time was in part supported by the Barnes‐Jewish Hospital Foundation. The authors report no conflicts of interest.
- , , , et al; Sepsis Occurrence in Acutely Ill Patients Investigators. Sepsis in European intensive care units: results of the SOAP study. Crit Care Med. 2006;34:344–353
- , , , et al. Death in the United States, 2007. NCHS Data Brief. 2009;26:1–8.
- , , , et al. The epidemiology of sepsis in the United States from 1979 through 2000. N Engl J Med. 2003;348:1548–1564.
- , , , , , . Epidemiology of severe sepsis in the United States: analysis of incidence, outcome, and associated costs of care. Crit Care Med. 2001;29:1303–1310.
- , , , et al: Hospitalizations, costs, and outcomes of severe sepsis in the United States 2003 to 2007. Crit Care Med. 2012;40:754–761.
- , , , et al. Facing the challenge: decreasing case fatality rates in severe sepsis despite increasing hospitalization. Crit Care Med. 2005;33:2555–2562.
- , , , et al. Rapid increase in hospitalization and mortality rates for severe sepsis in the United States: a trend analysis from 1993 to 2003. Crit Care Med. 2007;35:1244–1250.
- , , , , . Two decades of mortality trends among patients with severe sepsis: a comparative meta‐analysis. Crit Care Med. 2014;42:625–631.
- , , , et al. Preventing 30‐day hospital readmissions: a systematic review and meta‐analysis of randomized trials. JAMA Intern Med. 2014;174:1095–1107.
- , . Trends in septicemia hospitalizations and readmissions in selected HCUP states, 2005 and 2010. HCUP Statistical Brief #161. Agency for Healthcare Research and Quality, Rockville, MD. Available at: http://www.hcup‐us.ahrq.gov/reports/statbriefs/sb161.pdf. Published September 2013, Accessed January 13, 2015.
- , , , . Early and late unplanned rehospitalizations for survivors of critical illness. Crit Care Med. 2015;43:430–438.
- , , , , , . Hospital readmission and healthcare utilization following sepsis in community settings. J Hosp Med. 2014;9:502–507.
- , , , , . Increased 1‐year healthcare use in survivors of severe sepsis. Am J Respir Crit Care Med. 2014;190:62–69.
- , , , , . Multi‐drug resistance, inappropriate initial antibiotic therapy and mortality in Gram‐negative severe sepsis and septic shock: a retrospective cohort study. Crit Care. 2014;18:596.
- , , . Does combination antimicrobial therapy reduce mortality in Gram‐negative bacteraemia? A meta‐analysis. Lancet Infect Dis. 2004;4:519–527.
- , , , et al. Multidrug‐resistant, extensively drug‐resistant and pandrug‐resistant bacteria: an international expert proposal for interim standard definitions for acquired resistance. Clin Microbiol Infect. 2012;18:268–281.
- , , , , ; Acute Dialysis Quality Initiative Workgroup. Acute renal failure—definition, outcome measures, animal models, fluid therapy and information technology needs: the Second International Consensus Conference of the Acute Dialysis Quality Initiative (ADQI) Group. Crit Care. 2004;8:R204–R212.
- , , , . APACHE II: a severity of disease classification system. Crit Care Med. 1985;13:818–829.
- , , , et al. RIFLE criteria for acute kidney injury are associated with hospital mortality in critically ill patients: a cohort analysis. Crit Care. 2006;10:R73
- , , , , , . The association of acute kidney injury in the critically ill and postdischarge outcomes: a cohort study. Crit Care Med. 2015;43:354–364.
- , , , . Frequency, cost, and risk factors of readmissions among severe sepsis survivors. Crit Care Med. 2015;43:738–746.
- Acute Kidney Injury Work Group. Kidney disease: improving global outcomes (KDIGO). KDIGO clinical practice guideline for acute kidney injury. Kidney Int Suppl. 2012;2:1–138. Available at: http://www.kdigo.org/clinical_practice_guidelines/pdf/KDIGO%20AKI%20Guideline.pdf. Accessed March 4, 2015.
- , , , , , . Validity of administrative data in recording sepsis: a systematic review. Crit Care. 2015;19(1):139.
- , , , , , . Severe sepsis cohorts derived from claims‐based strategies appear to be biased toward a more severely ill patient population. Crit Care Med. 2013;41:945–953.
Despite its decreasing mortality, sepsis remains a leading reason for intensive care unit (ICU) admission and is associated with crude mortality in excess of 25%.[1, 2] In the United States there are between 660,000 and 750,000 sepsis hospitalizations annually, with the direct costs surpassing $24 billion.[3, 4, 5] As mortality rates have begun to fall, attention has shifted to issues of morbidity and recovery, the intermediate and longer‐term consequences associated with survivorship, and how interventions made while the patient is acutely ill in the ICU alter later health outcomes.[3, 5, 6, 7, 8]
One area of particular interest is the need for healthcare utilization following an acute admission for sepsis, and specifically rehospitalization within 30 days of discharge. This outcome is important not just from the perspective of the patient's well‐being, but also from the point of view of healthcare financing. Through the establishment of Hospital Readmission Reduction Program, the Centers for Medicare and Medicaid Services have sharply reduced reimbursement to hospitals for excessive rates of 30‐day readmissions.[9]
For sepsis, little is known about such readmissions, and even less about how to prevent them. A handful of studies suggest that this rate is between 5% and 26%.[10, 11, 12, 13] Whereas some of these studies looked at some of the factors that impact readmissions,[11, 12] none examined the potential contribution of microbiology of sepsis to this outcome.
To explore these questions, we conducted a single‐center retrospective cohort study among critically ill patients admitted to the ICU with severe culture‐positive sepsis and/or septic shock and determined the rate of early posthospital discharge readmission. In addition, we sought to elucidate predictors of subsequent readmission.
METHODS
Study Design and Ethical Standards
We conducted a single‐center retrospective cohort study from January 2008 to December 2012. The study was approved by the Washington University School of Medicine Human Studies Committee, and informed consent was waived because the data collection was retrospective without any patient‐identifying information. The study was performed in accordance with the ethical standards of the 1964 Declaration of Helsinki and its later amendments. Aspects of our methodology have been previously published.[14]
Primary Endpoint
All‐cause readmission to an acute‐care facility in the 30 days following discharge after the index hospitalization with sepsis served as the primary endpoint. The index hospitalizations occurred at the Barnes‐Jewish Hospital, a 1200‐bed inner‐city academic institution that serves as the main teaching institution for BJC HealthCare, a large integrated healthcare system of both inpatient and outpatient care. BJC includes a total of 13 hospitals in a compact geographic region surrounding and including St. Louis, Missouri, and we included readmission to any of these hospitals in our analysis. Persons treated within this healthcare system are, in nearly all cases, readmitted to 1 of the system's participating 13 hospitals. If a patient who receives healthcare in the system presents to an out‐of‐system hospital, he/she is often transferred back into the integrated system because of issues of insurance coverage.
Study Cohort
All consecutive adult ICU patients were included if (1) They had a positive blood culture for a pathogen (Cultures positive only for coagulase negative Staphylococcus aureus were excluded as contaminants.), (2) there was an International Classification of Diseases, Ninth Revision, Clinical Modification (ICD‐9‐CM) code corresponding to an acute organ dysfunction,[4] and (3) they survived their index hospitalization. Only the first episode of sepsis was included as the index hospitalization.
Definitions
All‐cause 30‐day readmission, was defined as a repeat hospitalization within 30 days of discharge from the index hospitalization among survivors of culture‐positive severe sepsis or septic shock. The definition of severe sepsis was based on discharge ICD‐9‐CM codes for acute organ dysfunction.[3] Patients were classified as having septic shock if vasopressors (norepinephrine, dopamine, epinephrine, phenylephrine, or vasopressin) were initiated within 24 hours of the blood culture collection date and time.
Initially appropriate antimicrobial treatment (IAAT) was deemed appropriate if the initially prescribed antibiotic regimen was active against the identified pathogen based on in vitro susceptibility testing and administered for at least 24 hours within 24 hours following blood culture collection. All other regimens were classified as non‐IAAT. Combination antimicrobial treatment was not required for IAAT designation.[15] Prior antibiotic exposure and prior hospitalization occurred within the preceding 90 days, and prior bacteremia within 30 days of the index episode. Multidrug resistance (MDR) among Gram‐negative bacteria was defined as nonsusceptibility to at least 1 antimicrobial agent from at least 3 different antimicrobial classes.[16] Both extended spectrum ‐lactamase (ESBL) organisms and carbapenemase‐producing Enterobacteriaceae were identified via molecular testing.
Healthcare‐associated (HCA) infections were defined by the presence of at least 1 of the following: (1) recent hospitalization, (2) immune suppression (defined as any primary immune deficiency or acquired immune deficiency syndrome or exposure within 3 prior months to immunosuppressive treatmentschemotherapy, radiation therapy, or steroids), (3) nursing home residence, (4) hemodialysis, (5) prior antibiotics. and (6) index bacteremia deemed a hospital‐acquired bloodstream infection (occurring >2 days following index admission date). Acute kidney injury (AKI) was defined according to the RIFLE (Risk, Injury, Failure, Loss, End‐stage) criteria based on the greatest change in serum creatinine (SCr).[17]
Data Elements
Patient‐specific baseline characteristics and process of care variables were collected from the automated hospital medical record, microbiology database, and pharmacy database of Barnes‐Jewish Hospital. Electronic inpatient and outpatient medical records available for all patients in the BJC HealthCare system were reviewed to determine prior antibiotic exposure. The baseline characteristics collected during the index hospitalization included demographics and comorbid conditions. The comorbidities were identified based on their corresponding ICD‐9‐CM codes. The Acute Physiology and Chronic Health Evaluation (APACHE) II and Charlson comorbidity scores were calculated based on clinical data present during the 24 hours after the positive blood cultures were obtained.[18] This was done to accommodate patients with community‐acquired and healthcare‐associated community‐onset infections who only had clinical data available after blood cultures were drawn. Lowest and highest SCr levels were collected during the index hospitalization to determine each patient's AKI status.
Statistical Analyses
Continuous variables were reported as means with standard deviations and as medians with 25th and 75th percentiles. Differences between mean values were tested via the Student t test, and between medians using the Mann‐Whitney U test. Categorical data were summarized as proportions, and the 2 test or Fisher exact test for small samples was used to examine differences between groups. We developed multiple logistic regression models to identify clinical risk factors that were associated with 30‐day all‐cause readmission. All risk factors that were significant at 0.20 in the univariate analyses, as well as all biologically plausible factors even if they did not reach this level of significance, were included in the models. All variables entered into the models were assessed for collinearity, and interaction terms were tested. The most parsimonious models were derived using the backward manual elimination method, and the best‐fitting model was chosen based on the area under the receiver operating characteristics curve (AUROC or the C statistic). The model's calibration was assessed with the Hosmer‐Lemeshow goodness‐of‐fit test. All tests were 2‐tailed, and a P value <0.05 represented statistical significance.
All computations were performed in Stata/SE, version 9 (StataCorp, College Station, TX).
Role of Sponsor
The sponsor had no role in the design, analyses, interpretation, or publication of the study.
RESULTS
Among the 1697 patients with severe sepsis or septic shock who were discharged alive from the hospital, 543 (32.0%) required a rehospitalization within 30 days. There were no differences in age or gender distribution between the groups (Table 1). All comorbidities examined were more prevalent among those with a 30‐day readmission than among those without, with the median Charlson comorbidity score reflecting this imbalance (5 vs 4, P<0.001). Similarly, most of the HCA risk factors were more prevalent among the readmitted group than the comparator group, with HCA sepsis among 94.2% of the former and 90.7% of the latter (P = 0.014).
| 30‐Day Readmission = Yes | 30‐Day Readmission = No | ||||
|---|---|---|---|---|---|
| N = 543 | % = 32.00% | N = 1,154 | % = 68.00% | P Value | |
| |||||
| Baseline characteristics | |||||
| Age, y | |||||
| Mean SD | 58.5 15.7 | 59.5 15.8 | |||
| Median (25, 75) | 60 (49, 69) | 60 (50, 70) | 0.297 | ||
| Race | |||||
| Caucasian | 335 | 61.69% | 769 | 66.64% | 0.046 |
| African American | 157 | 28.91% | 305 | 26.43% | 0.284 |
| Other | 9 | 1.66% | 22 | 1.91% | 0.721 |
| Sex, female | 244 | 44.94% | 537 | 46.53% | 0.538 |
| Admission source | |||||
| Home | 374 | 68.88% | 726 | 62.91% | 0.016 |
| Nursing home, rehab, or LTAC | 39 | 7.81% | 104 | 9.01% | 0.206 |
| Transfer from another hospital | 117 | 21.55% | 297 | 25.74% | 0.061 |
| Comorbidities | |||||
| CHF | 131 | 24.13% | 227 | 19.67% | 0.036 |
| COPD | 156 | 28.73% | 253 | 21.92% | 0.002 |
| CLD | 83 | 15.29% | 144 | 12.48% | 0.113 |
| DM | 175 | 32.23% | 296 | 25.65% | 0.005 |
| CKD | 137 | 25.23% | 199 | 17.24% | <0.001 |
| Malignancy | 225 | 41.44% | 395 | 34.23% | 0.004 |
| HIV | 11 | 2.03% | 10 | 0.87% | 0.044 |
| Charlson comorbidity score | |||||
| Mean SD | 5.24 3.32 | 4.48 3.35 | |||
| Median (25, 75) | 5 (3, 8) | 4 (2, 7) | <0.001 | ||
| HCA RF | 503 | 94.19% | 1,019 | 90.66% | 0.014 |
| Hemodialysis | 65 | 12.01% | 114 | 9.92% | 0.192 |
| Immune suppression | 193 | 36.07% | 352 | 31.21% | 0.044 |
| Prior hospitalization | 339 | 65.07% | 620 | 57.09% | 0.002 |
| Nursing home residence | 39 | 7.81% | 104 | 9.01% | 0.206 |
| Prior antibiotics | 301 | 55.43% | 568 | 49.22% | 0.017 |
| Hospital‐acquired BSI* | 240 | 44.20% | 485 | 42.03% | 0.399 |
| Prior bacteremia within 30 days | 88 | 16.21% | 154 | 13.34% | 0.116 |
| Sepsis‐related parameters | |||||
| LOS prior to bacteremia, d | |||||
| Mean SD | 6.65 11.22 | 5.88 10.81 | |||
| Median (25, 75) | 1 (0, 10) | 0 (0, 8) | 0.250 | ||
| Surgery | |||||
| None | 362 | 66.67% | 836 | 72.44% | 0.015 |
| Abdominal | 104 | 19.15% | 167 | 14.47% | 0.014 |
| Extra‐abdominal | 73 | 13.44% | 135 | 11.70% | 0.306 |
| Status unknown | 4 | 0.74% | 16 | 1.39% | 0.247 |
| Central line | 333 | 64.41% | 637 | 57.80% | 0.011 |
| TPN at the time of bacteremia or prior to it during index hospitalization | 52 | 9.74% | 74 | 5.45% | 0.017 |
| APACHE II | |||||
| Mean SD | 15.08 5.47 | 15.35 5.43 | |||
| Median (25, 75) | 15 (11, 18) | 15 (12, 19) | 0.275 | ||
| Severe sepsis | 361 | 66.48% | 747 | 64.73% | 0.480 |
| Septic shock requiring vasopressors | 182 | 33.52% | 407 | 35.27% | |
| On MV | 104 | 19.22% | 251 | 21.90% | 0.208 |
| Peak WBC (103/L) | |||||
| Mean SD | 22.26 25.20 | 22.14 17.99 | |||
| Median (25, 75) | 17.1 (8.9, 30.6) | 16.9 (10, 31) | 0.654 | ||
| Lowest serum SCr, mg/dL | |||||
| Mean SD | 1.02 1.05 | 0.96 1.03 | |||
| Median (25, 75) | 0.68 (0.5, 1.06) | 0.66 (0.49, 0.96) | 0.006 | ||
| Highest serum SCr, mg/dL | |||||
| Mean SD | 2.81 2.79 | 2.46 2.67 | |||
| Median (25, 75) | 1.68 (1.04, 3.3) | 1.41 (0.94, 2.61) | 0.001 | ||
| RIFLE category | |||||
| None | 81 | 14.92% | 213 | 18.46% | 0.073 |
| Risk | 112 | 20.63% | 306 | 26.52% | 0.009 |
| Injury | 133 | 24.49% | 247 | 21.40% | 0.154 |
| Failure | 120 | 22.10% | 212 | 18.37% | 0.071 |
| Loss | 50 | 9.21% | 91 | 7.89% | 0.357 |
| End‐stage | 47 | 8.66% | 85 | 7.37% | 0.355 |
| Infection source | |||||
| Urine | 95 | 17.50% | 258 | 22.36% | 0.021 |
| Abdomen | 69 | 12.71% | 113 | 9.79% | 0.070 |
| Lung | 93 | 17.13% | 232 | 20.10% | 0.146 |
| Line | 91 | 16.76% | 150 | 13.00% | 0.038 |
| CNS | 1 | 0.18% | 16 | 1.39% | 0.012 |
| Skin | 51 | 9.39% | 82 | 7.11% | 0.102 |
| Unknown | 173 | 31.86% | 375 | 32.50% | 0.794 |
During the index hospitalization, 589 patients (34.7%) suffered from septic shock requiring vasopressors; this did not impact the 30‐day readmission risk (Table 1). Commensurately, markers of severity of acute illness (APACHE II score, mechanical ventilation, peak white blood cell count) did not differ between the groups. With respect to the primary source of sepsis, urine was less, whereas central nervous system was more likely among those readmitted within 30 days. Similarly, there was a significant imbalance between the groups in the prevalence of AKI (Table 1). Specifically, those who did require a readmission were slightly less likely to have sustained no AKI (RIFLE: None; 14.9% vs 18.5%, P = 0.073). Those requiring readmission were also less likely to be in the category RIFLE: Risk (20.6% vs 26.5%, P = 0.009). The direction of this disparity was reversed for the Injury and Failure categories. No differences between groups were seen among those with categories Loss and end‐stage kidney disease (ESKD) (Table 1).
The microbiology of sepsis did not differ in most respects between the 30‐day readmission groups, save for several organisms (Table 2). Most strikingly, those who required a readmission were more likely than those who did not to be infected with Bacteroides spp, Candida spp, an MDR or an ESBL organism (Table 2). As for the outcomes of the index hospitalization, those with a repeat admission had a longer overall and postonset of sepsis initial hospital length of stay, and were less likely to be discharged either home without home health care or transferred to another hospital at the end of their index hospitalization (Table 3).
| 30‐Day Readmission = Yes | 30‐Day Readmission = No | P Value | |||
|---|---|---|---|---|---|
| N | % | N | % | ||
| |||||
| 543 | 32.00% | 1,154 | 68.00% | ||
| Gram‐positive BSI | 260 | 47.88% | 580 | 50.26% | 0.376 |
| Staphylococcus aureus | 138 | 25.41% | 287 | 24.87% | 0.810 |
| MRSA | 78 | 14.36% | 147 | 12.74% | 0.358 |
| VISA | 6 | 1.10% | 9 | 0.78% | 0.580 |
| Streptococcus pneumoniae | 7 | 1.29% | 33 | 2.86% | 0.058 |
| Streptococcus spp | 34 | 6.26% | 81 | 7.02% | 0.606 |
| Peptostreptococcus spp | 5 | 0.92% | 15 | 1.30% | 0.633 |
| Clostridium perfringens | 4 | 0.74% | 10 | 0.87% | 1.000 |
| Enterococcus faecalis | 54 | 9.94% | 108 | 9.36% | 0.732 |
| Enterococcus faecium | 29 | 5.34% | 63 | 5.46% | 1.000 |
| VRE | 36 | 6.63% | 70 | 6.07% | 0.668 |
| Gram‐negative BSI | 231 | 42.54% | 515 | 44.63% | 0.419 |
| Escherichia coli | 54 | 9.94% | 151 | 13.08% | 0.067 |
| Klebsiella pneumoniae | 54 | 9.94% | 108 | 9.36% | 0.723 |
| Klebsiella oxytoca | 11 | 2.03% | 18 | 1.56% | 0.548 |
| Enterobacter aerogenes | 6 | 1.10% | 13 | 1.13% | 1.000 |
| Enterobacter cloacae | 21 | 3.87% | 44 | 3.81% | 1.000 |
| Pseudomonas aeruginosa | 28 | 5.16% | 65 | 5.63% | 0.733 |
| Acinetobacter spp | 8 | 1.47% | 27 | 2.34% | 0.276 |
| Bacteroides spp | 25 | 4.60% | 30 | 2.60% | 0.039 |
| Serratia marcescens | 14 | 2.58% | 21 | 1.82% | 0.360 |
| Stenotrophomonas maltophilia | 3 | 0.55% | 8 | 0.69% | 1.000 |
| Achromobacter spp | 2 | 0.37% | 3 | 0.17% | 0.597 |
| Aeromonas spp | 2 | 0.37% | 1 | 0.09% | 0.241 |
| Burkholderia cepacia | 0 | 0.00% | 6 | 0.52% | 0.186 |
| Citrobacter freundii | 2 | 0.37% | 15 | 1.39% | 0.073 |
| Fusobacterium spp | 7 | 1.29% | 10 | 0.87% | 0.438 |
| Haemophilus influenzae | 1 | 0.18% | 4 | 0.35% | 1.000 |
| Prevotella spp | 1 | 0.18% | 6 | 0.52% | 0.441 |
| Proteus mirabilis | 9 | 1.66% | 39 | 3.38% | 0.058 |
| MDR PA | 2 | 0.37% | 7 | 0.61% | 0.727 |
| ESBL | 10 | 6.25% | 8 | 2.06% | 0.017 |
| CRE | 2 | 1.25% | 0 | 0.00% | 0.028 |
| MDR Gram‐negative or Gram‐positive | 231 | 47.53% | 450 | 41.86% | 0.036 |
| Candida spp | 58 | 10.68% | 76 | 6.59% | 0.004 |
| Polymicrobal BSI | 50 | 9.21% | 111 | 9.62% | 0.788 |
| Initially inappropriate treatment | 119 | 21.92% | 207 | 17.94% | 0.052 |
| 30‐Day Readmission = Yes | 30‐Day Readmission = No | ||||
|---|---|---|---|---|---|
| N = 543 | % = 32.00% | N = 1,154 | % = 68.00% | P Value | |
| |||||
| Hospital LOS, days | |||||
| Mean SD | 26.44 23.27 | 23.58 21.79 | 0.019 | ||
| Median (25, 75) | 19.16 (9.66, 35.86) | 17.77 (8.9, 30.69) | |||
| Hospital LOS following BSI onset, days | |||||
| Mean SD | 19.80 18.54 | 17.69 17.08 | 0.022 | ||
| Median (25, 75) | 13.9 (7.9, 25.39) | 12.66 (7.05, 22.66) | |||
| Discharge destination | |||||
| Home | 125 | 23.02% | 334 | 28.94% | 0.010 |
| Home with home care | 163 | 30.02% | 303 | 26.26% | 0.105 |
| Rehab | 81 | 14.92% | 149 | 12.91% | 0.260 |
| LTAC | 41 | 7.55% | 87 | 7.54% | 0.993 |
| Transfer to another hospital | 1 | 0.18% | 19 | 1.65% | 0.007 |
| SNF | 132 | 24.31% | 262 | 22.70% | 0.465 |
In a logistic regression model, 5 factors emerged as predictors of 30‐day readmission (Table 4). Having RIFLE: Injury or RIFLE: Failure carried an approximately 2‐fold increase in the odds of 30‐day rehospitalization (odds ratio: 1.95, 95% confidence interval: 1.302.93, P = 0.001) relative to having a RIFLE: None or RIFLE: Risk. Although having strong association with this outcome, harboring an ESBL organism or Bacteroides spp were both relatively infrequent events (3.3% ESBL and 3.2% Bacteroides spp). Infection with Escherichia coli and urine as the source of sepsis both appeared to be significantly protective against a readmission (Table 4). The model's discrimination was moderate (AUROC = 0.653) and its calibration adequate (Hosmer‐Lemeshow P = 0.907). (See Supporting Information, Appendix 1, in the online version of this article for the steps in the development of the final model.)
| OR | 95% CI | P Value | |
|---|---|---|---|
| |||
| ESBL | 4.503 | 1.42914.190 | 0.010 |
| RIFLE: Injury or Failure (reference: RIFLE: None or Risk) | 1.951 | 1.2972.933 | 0.001 |
| Bacteroides spp | 2.044 | 1.0583.948 | 0.033 |
| Source: urine | 0.583 | 0.3470.979 | 0.041 |
| Escherichia coli | 0.494 | 0.2700.904 | 0.022 |
DISCUSSION
In this single‐center retrospective cohort study, nearly one‐third of survivors of culture‐positive severe sepsis or septic shock required a rehospitalization within 30 days of discharge from their index admission. Factors that contributed to a higher odds of rehospitalization were having mild‐to‐moderate AKI (RIFLE: Injury or RIFLE: Failure) and infection with ESBL organisms or Bacteroides spp, whereas urine as the source of sepsis and E coli as the pathogen appeared to be protective.
A recent study by Hua and colleagues examining the New York Statewide Planning and Research Cooperative System for the years 2008 to 2010 noted a 16.2% overall rate of 30‐day rehospitalization among survivors of initial critical illness.[11] Just as we observed, Hua et al. concluded that development of AKI correlated with readmission. Because they relied on administrative data for their analysis, AKI was diagnosed when hemodialysis was utilized. Examining AKI using SCr changes, our findings add a layer of granularity to the relationship between AKI stages and early readmission. Specifically, we failed to detect any rise in the odds of rehospitalization when either very mild (RIFLE: Risk) or severe (RIFLE: Loss or RIFLE: ESKD) AKI was present. Only when either RIFLE: Injury or RIFLE: Failure developed did the odds of readmission rise. In addition to diverging definitions between our studies, differences in populations also likely yielded different results.[11] Although Hua et al. examined all admissions to the ICU regardless of the diagnosis or illness severity, our cohort consisted of only those ICU patients who survived culture‐positive severe sepsis/septic shock. Because AKI is a known risk factor for mortality in sepsis,[19] the potential for immortal time bias leaves a smaller pool of surviving patients with ESKD at risk for readmission. Regardless of the explanation, it may be prudent to focus on preventing AKI not only to improve survival, but also from the standpoint of diminishing the risk of an early readmission.
Four additional studies have examined the frequency of early readmissions among survivors of critical illness. Liu et al. noted 17.9% 30‐day rehospitalization rate among sepsis survivors.[12] Factors associated with the risk of early readmission included acute and chronic diseases burdens, index hospital LOS, and the need for the ICU in the index sepsis admission. In contrast to our cohort, all of whom were in the ICU during their index episode, less than two‐thirds of the entire population studied by Liu had required an ICU admission. Additionally, Liu's study did not specifically examine the potential impact of AKI or of microbiology on this outcome.
Prescott and coworkers examined healthcare utilization following an episode of severe sepsis.[13] Among other findings, they reported a 30‐day readmission rate of 26.5% among survivors. Although closer to our estimate, this study included all patients surviving a severe sepsis hospitalization, and not only those with a positive culture. These investigators did not examine predictors of readmission.[13]
Horkan et al. examined specifically whether there was an association between AKI and postdischarge outcomes, including 30‐day readmission risk, in a large cohort of patients who survived their critical illness.[20] In it they found that readmission risk ranged from 19% to 21%, depending on the extent of the AKI. Moreover, similar to our findings, they reported that in an adjusted analysis RIFLE: Injury and RIFLE: Failure were associated with a rise in the odds of a 30‐day rehospitalizaiton. In contrast to our study, Horkan et al. did detect an increase in the odds of this outcome associated with RIFLE: Risk. There are likely at least 3 reasons for this difference. First, we focused only on patients with severe sepsis or septic shock, whereas Horkan and colleagues included all critical illness survivors. Second, we were able to explore the impact of microbiology on this outcome. Third, Horkan's study included an order of magnitude more patients than did ours, thus making it more likely either to have the power to detect a true association that we may have lacked or to be more susceptible to type I error.
Finally, Goodwin and colleagues utilized 3 states' databases included in the Health Care and Utilization Project (HCUP) from the Agency for Healthcare Research and Quality to study frequency and risk factors for 30‐day readmission among survivors of severe sepsis.[21] Patients were identified based on the use of the severe sepsis (995.92) and septic shock (785.52). These authors found a 30‐day readmission rate of 26%. Although chronic renal disease, among several other factors, was associated with an increase in this risk, the data source did not permit these investigators to examine the impact of AKI on the outcomes. Similarly, HCUP data do not contain microbiology, a distinct difference from our analysis.
If clinicians are to pursue strategies to reduce the risk of an all‐cause 30‐day readmission, the key goal is not simply to identify all variables associated with readmission, but to focus on factors that are potentially modifiable. Although neither Hua nor Liu and their teams identified any additional factors that are potentially modifiable,[11, 12] in the present study, among the 5 factors we identified, the development of mild to moderate AKI during the index hospitalization may deserve stronger consideration for efforts at prevention. Although one cannot conclude automatically that preventing AKI in this population could mitigate some of the early rehospitalization risk, critically ill patients are frequently exposed to a multitude of nephrotoxic agents. Those caring for subjects with sepsis should reevaluate the risk‐benefit equation of these factors more cautiously and apply guideline‐recommended AKI prevention strategies more aggressively, particularly because a relatively minor change in SCr resulted in an excess risk of readmission.[22]
In addition to AKI, which is potentially modifiable, we identified several other clinical factors predictive of 30‐day readmission, which are admittedly not preventable. Thus, microbiology was predictive of this outcome, with E coli engendering fewer and Bacteroides spp and ESBL organisms more early rehospitalizations. Similarly, urine as the source of sepsis was associated with a lower risk for this endpoint.
Our study has a number of limitations. As a retrospective cohort, it is subject to bias, most notably a selection bias. Specifically, because the flagship hospital of the BJC HealthCare system is a referral center, it is possible that we did not capture all readmissions. However, generally, if a patient who receives healthcare within 1 of the BJC hospitals presents to a nonsystem hospital, that patient is nearly always transferred back into the integrated system because of issues of insurance coverage. Analysis of certain diagnosis‐related groups has indicated that 73% of all patients overall discharged from 4 of the large BJC system institutions who require a readmission within 30 days of discharge return to a BJC hospital (personal communication, Financial Analysis and Decision Support Department at BJC to Dr. Kollef May 12, 2015). Therefore, we may have misclassified the outcome in as many as 180 patients. The fact that our readmission rate was fully double that seen in Hua et al.'s and Liu et al.'s studies, and somewhat higher than that reported by Prescott et al., attests not only to the population differences, but also to the fact that we are unlikely to have missed a substantial percentage of readmissions.[11, 12, 13] Furthermore, to mitigate biases, we enrolled all consecutive patients meeting the predetermined criteria. Missing from our analysis are events that occurred between the index discharge and the readmission. Likewise, we were unable to obtain such potentially important variables as code status or outpatient mortality following discharge. These intervening factors, if included in subsequent studies, may increase the predictive power of the model. Because we relied on administrative coding to identify cases of severe sepsis and septic shock, it is possible that there is misclassification within our cohort. Recent studies indicate, however, that the Angus definition, used in our study, has high negative and positive predictive values for severe sepsis identification.[23] It is still possible that our cohort is skewed toward a more severely ill population, making our results less generalizable to the less severely ill septic patients.[24] The study was performed at a single healthcare system and included only cases of severe sepsis or septic shock that had a positive blood culture, and thus the findings may not be broadly generalizable either to patients without a positive blood culture or to institutions that do not resemble it.
In summary, we have demonstrated that survivors of culture‐positive severe sepsis or septic shock have a high rate of 30‐day rehospitalization. Because the US federal government's initiatives deem 30‐day readmissions to be a quality metric and penalize institutions with higher‐than average readmission rates, a high volume of critically ill patients with culture‐positive severe sepsis and septic shock may disproportionately put an institution at risk for such penalties. Unfortunately, not many of the determinants of readmission are amenable to prevention. As sepsis survival continues to improve, hospitals will need to concentrate their resources on coordinating care of these complex patients so as to improve both individual quality of life and the quality of care that they provide.
Disclosures
This study was supported by a research grant from Cubist Pharmaceuticals, Lexington, Massachusetts. Dr. Kollef's time was in part supported by the Barnes‐Jewish Hospital Foundation. The authors report no conflicts of interest.
Despite its decreasing mortality, sepsis remains a leading reason for intensive care unit (ICU) admission and is associated with crude mortality in excess of 25%.[1, 2] In the United States there are between 660,000 and 750,000 sepsis hospitalizations annually, with the direct costs surpassing $24 billion.[3, 4, 5] As mortality rates have begun to fall, attention has shifted to issues of morbidity and recovery, the intermediate and longer‐term consequences associated with survivorship, and how interventions made while the patient is acutely ill in the ICU alter later health outcomes.[3, 5, 6, 7, 8]
One area of particular interest is the need for healthcare utilization following an acute admission for sepsis, and specifically rehospitalization within 30 days of discharge. This outcome is important not just from the perspective of the patient's well‐being, but also from the point of view of healthcare financing. Through the establishment of Hospital Readmission Reduction Program, the Centers for Medicare and Medicaid Services have sharply reduced reimbursement to hospitals for excessive rates of 30‐day readmissions.[9]
For sepsis, little is known about such readmissions, and even less about how to prevent them. A handful of studies suggest that this rate is between 5% and 26%.[10, 11, 12, 13] Whereas some of these studies looked at some of the factors that impact readmissions,[11, 12] none examined the potential contribution of microbiology of sepsis to this outcome.
To explore these questions, we conducted a single‐center retrospective cohort study among critically ill patients admitted to the ICU with severe culture‐positive sepsis and/or septic shock and determined the rate of early posthospital discharge readmission. In addition, we sought to elucidate predictors of subsequent readmission.
METHODS
Study Design and Ethical Standards
We conducted a single‐center retrospective cohort study from January 2008 to December 2012. The study was approved by the Washington University School of Medicine Human Studies Committee, and informed consent was waived because the data collection was retrospective without any patient‐identifying information. The study was performed in accordance with the ethical standards of the 1964 Declaration of Helsinki and its later amendments. Aspects of our methodology have been previously published.[14]
Primary Endpoint
All‐cause readmission to an acute‐care facility in the 30 days following discharge after the index hospitalization with sepsis served as the primary endpoint. The index hospitalizations occurred at the Barnes‐Jewish Hospital, a 1200‐bed inner‐city academic institution that serves as the main teaching institution for BJC HealthCare, a large integrated healthcare system of both inpatient and outpatient care. BJC includes a total of 13 hospitals in a compact geographic region surrounding and including St. Louis, Missouri, and we included readmission to any of these hospitals in our analysis. Persons treated within this healthcare system are, in nearly all cases, readmitted to 1 of the system's participating 13 hospitals. If a patient who receives healthcare in the system presents to an out‐of‐system hospital, he/she is often transferred back into the integrated system because of issues of insurance coverage.
Study Cohort
All consecutive adult ICU patients were included if (1) They had a positive blood culture for a pathogen (Cultures positive only for coagulase negative Staphylococcus aureus were excluded as contaminants.), (2) there was an International Classification of Diseases, Ninth Revision, Clinical Modification (ICD‐9‐CM) code corresponding to an acute organ dysfunction,[4] and (3) they survived their index hospitalization. Only the first episode of sepsis was included as the index hospitalization.
Definitions
All‐cause 30‐day readmission, was defined as a repeat hospitalization within 30 days of discharge from the index hospitalization among survivors of culture‐positive severe sepsis or septic shock. The definition of severe sepsis was based on discharge ICD‐9‐CM codes for acute organ dysfunction.[3] Patients were classified as having septic shock if vasopressors (norepinephrine, dopamine, epinephrine, phenylephrine, or vasopressin) were initiated within 24 hours of the blood culture collection date and time.
Initially appropriate antimicrobial treatment (IAAT) was deemed appropriate if the initially prescribed antibiotic regimen was active against the identified pathogen based on in vitro susceptibility testing and administered for at least 24 hours within 24 hours following blood culture collection. All other regimens were classified as non‐IAAT. Combination antimicrobial treatment was not required for IAAT designation.[15] Prior antibiotic exposure and prior hospitalization occurred within the preceding 90 days, and prior bacteremia within 30 days of the index episode. Multidrug resistance (MDR) among Gram‐negative bacteria was defined as nonsusceptibility to at least 1 antimicrobial agent from at least 3 different antimicrobial classes.[16] Both extended spectrum ‐lactamase (ESBL) organisms and carbapenemase‐producing Enterobacteriaceae were identified via molecular testing.
Healthcare‐associated (HCA) infections were defined by the presence of at least 1 of the following: (1) recent hospitalization, (2) immune suppression (defined as any primary immune deficiency or acquired immune deficiency syndrome or exposure within 3 prior months to immunosuppressive treatmentschemotherapy, radiation therapy, or steroids), (3) nursing home residence, (4) hemodialysis, (5) prior antibiotics. and (6) index bacteremia deemed a hospital‐acquired bloodstream infection (occurring >2 days following index admission date). Acute kidney injury (AKI) was defined according to the RIFLE (Risk, Injury, Failure, Loss, End‐stage) criteria based on the greatest change in serum creatinine (SCr).[17]
Data Elements
Patient‐specific baseline characteristics and process of care variables were collected from the automated hospital medical record, microbiology database, and pharmacy database of Barnes‐Jewish Hospital. Electronic inpatient and outpatient medical records available for all patients in the BJC HealthCare system were reviewed to determine prior antibiotic exposure. The baseline characteristics collected during the index hospitalization included demographics and comorbid conditions. The comorbidities were identified based on their corresponding ICD‐9‐CM codes. The Acute Physiology and Chronic Health Evaluation (APACHE) II and Charlson comorbidity scores were calculated based on clinical data present during the 24 hours after the positive blood cultures were obtained.[18] This was done to accommodate patients with community‐acquired and healthcare‐associated community‐onset infections who only had clinical data available after blood cultures were drawn. Lowest and highest SCr levels were collected during the index hospitalization to determine each patient's AKI status.
Statistical Analyses
Continuous variables were reported as means with standard deviations and as medians with 25th and 75th percentiles. Differences between mean values were tested via the Student t test, and between medians using the Mann‐Whitney U test. Categorical data were summarized as proportions, and the 2 test or Fisher exact test for small samples was used to examine differences between groups. We developed multiple logistic regression models to identify clinical risk factors that were associated with 30‐day all‐cause readmission. All risk factors that were significant at 0.20 in the univariate analyses, as well as all biologically plausible factors even if they did not reach this level of significance, were included in the models. All variables entered into the models were assessed for collinearity, and interaction terms were tested. The most parsimonious models were derived using the backward manual elimination method, and the best‐fitting model was chosen based on the area under the receiver operating characteristics curve (AUROC or the C statistic). The model's calibration was assessed with the Hosmer‐Lemeshow goodness‐of‐fit test. All tests were 2‐tailed, and a P value <0.05 represented statistical significance.
All computations were performed in Stata/SE, version 9 (StataCorp, College Station, TX).
Role of Sponsor
The sponsor had no role in the design, analyses, interpretation, or publication of the study.
RESULTS
Among the 1697 patients with severe sepsis or septic shock who were discharged alive from the hospital, 543 (32.0%) required a rehospitalization within 30 days. There were no differences in age or gender distribution between the groups (Table 1). All comorbidities examined were more prevalent among those with a 30‐day readmission than among those without, with the median Charlson comorbidity score reflecting this imbalance (5 vs 4, P<0.001). Similarly, most of the HCA risk factors were more prevalent among the readmitted group than the comparator group, with HCA sepsis among 94.2% of the former and 90.7% of the latter (P = 0.014).
| 30‐Day Readmission = Yes | 30‐Day Readmission = No | ||||
|---|---|---|---|---|---|
| N = 543 | % = 32.00% | N = 1,154 | % = 68.00% | P Value | |
| |||||
| Baseline characteristics | |||||
| Age, y | |||||
| Mean SD | 58.5 15.7 | 59.5 15.8 | |||
| Median (25, 75) | 60 (49, 69) | 60 (50, 70) | 0.297 | ||
| Race | |||||
| Caucasian | 335 | 61.69% | 769 | 66.64% | 0.046 |
| African American | 157 | 28.91% | 305 | 26.43% | 0.284 |
| Other | 9 | 1.66% | 22 | 1.91% | 0.721 |
| Sex, female | 244 | 44.94% | 537 | 46.53% | 0.538 |
| Admission source | |||||
| Home | 374 | 68.88% | 726 | 62.91% | 0.016 |
| Nursing home, rehab, or LTAC | 39 | 7.81% | 104 | 9.01% | 0.206 |
| Transfer from another hospital | 117 | 21.55% | 297 | 25.74% | 0.061 |
| Comorbidities | |||||
| CHF | 131 | 24.13% | 227 | 19.67% | 0.036 |
| COPD | 156 | 28.73% | 253 | 21.92% | 0.002 |
| CLD | 83 | 15.29% | 144 | 12.48% | 0.113 |
| DM | 175 | 32.23% | 296 | 25.65% | 0.005 |
| CKD | 137 | 25.23% | 199 | 17.24% | <0.001 |
| Malignancy | 225 | 41.44% | 395 | 34.23% | 0.004 |
| HIV | 11 | 2.03% | 10 | 0.87% | 0.044 |
| Charlson comorbidity score | |||||
| Mean SD | 5.24 3.32 | 4.48 3.35 | |||
| Median (25, 75) | 5 (3, 8) | 4 (2, 7) | <0.001 | ||
| HCA RF | 503 | 94.19% | 1,019 | 90.66% | 0.014 |
| Hemodialysis | 65 | 12.01% | 114 | 9.92% | 0.192 |
| Immune suppression | 193 | 36.07% | 352 | 31.21% | 0.044 |
| Prior hospitalization | 339 | 65.07% | 620 | 57.09% | 0.002 |
| Nursing home residence | 39 | 7.81% | 104 | 9.01% | 0.206 |
| Prior antibiotics | 301 | 55.43% | 568 | 49.22% | 0.017 |
| Hospital‐acquired BSI* | 240 | 44.20% | 485 | 42.03% | 0.399 |
| Prior bacteremia within 30 days | 88 | 16.21% | 154 | 13.34% | 0.116 |
| Sepsis‐related parameters | |||||
| LOS prior to bacteremia, d | |||||
| Mean SD | 6.65 11.22 | 5.88 10.81 | |||
| Median (25, 75) | 1 (0, 10) | 0 (0, 8) | 0.250 | ||
| Surgery | |||||
| None | 362 | 66.67% | 836 | 72.44% | 0.015 |
| Abdominal | 104 | 19.15% | 167 | 14.47% | 0.014 |
| Extra‐abdominal | 73 | 13.44% | 135 | 11.70% | 0.306 |
| Status unknown | 4 | 0.74% | 16 | 1.39% | 0.247 |
| Central line | 333 | 64.41% | 637 | 57.80% | 0.011 |
| TPN at the time of bacteremia or prior to it during index hospitalization | 52 | 9.74% | 74 | 5.45% | 0.017 |
| APACHE II | |||||
| Mean SD | 15.08 5.47 | 15.35 5.43 | |||
| Median (25, 75) | 15 (11, 18) | 15 (12, 19) | 0.275 | ||
| Severe sepsis | 361 | 66.48% | 747 | 64.73% | 0.480 |
| Septic shock requiring vasopressors | 182 | 33.52% | 407 | 35.27% | |
| On MV | 104 | 19.22% | 251 | 21.90% | 0.208 |
| Peak WBC (103/L) | |||||
| Mean SD | 22.26 25.20 | 22.14 17.99 | |||
| Median (25, 75) | 17.1 (8.9, 30.6) | 16.9 (10, 31) | 0.654 | ||
| Lowest serum SCr, mg/dL | |||||
| Mean SD | 1.02 1.05 | 0.96 1.03 | |||
| Median (25, 75) | 0.68 (0.5, 1.06) | 0.66 (0.49, 0.96) | 0.006 | ||
| Highest serum SCr, mg/dL | |||||
| Mean SD | 2.81 2.79 | 2.46 2.67 | |||
| Median (25, 75) | 1.68 (1.04, 3.3) | 1.41 (0.94, 2.61) | 0.001 | ||
| RIFLE category | |||||
| None | 81 | 14.92% | 213 | 18.46% | 0.073 |
| Risk | 112 | 20.63% | 306 | 26.52% | 0.009 |
| Injury | 133 | 24.49% | 247 | 21.40% | 0.154 |
| Failure | 120 | 22.10% | 212 | 18.37% | 0.071 |
| Loss | 50 | 9.21% | 91 | 7.89% | 0.357 |
| End‐stage | 47 | 8.66% | 85 | 7.37% | 0.355 |
| Infection source | |||||
| Urine | 95 | 17.50% | 258 | 22.36% | 0.021 |
| Abdomen | 69 | 12.71% | 113 | 9.79% | 0.070 |
| Lung | 93 | 17.13% | 232 | 20.10% | 0.146 |
| Line | 91 | 16.76% | 150 | 13.00% | 0.038 |
| CNS | 1 | 0.18% | 16 | 1.39% | 0.012 |
| Skin | 51 | 9.39% | 82 | 7.11% | 0.102 |
| Unknown | 173 | 31.86% | 375 | 32.50% | 0.794 |
During the index hospitalization, 589 patients (34.7%) suffered from septic shock requiring vasopressors; this did not impact the 30‐day readmission risk (Table 1). Commensurately, markers of severity of acute illness (APACHE II score, mechanical ventilation, peak white blood cell count) did not differ between the groups. With respect to the primary source of sepsis, urine was less, whereas central nervous system was more likely among those readmitted within 30 days. Similarly, there was a significant imbalance between the groups in the prevalence of AKI (Table 1). Specifically, those who did require a readmission were slightly less likely to have sustained no AKI (RIFLE: None; 14.9% vs 18.5%, P = 0.073). Those requiring readmission were also less likely to be in the category RIFLE: Risk (20.6% vs 26.5%, P = 0.009). The direction of this disparity was reversed for the Injury and Failure categories. No differences between groups were seen among those with categories Loss and end‐stage kidney disease (ESKD) (Table 1).
The microbiology of sepsis did not differ in most respects between the 30‐day readmission groups, save for several organisms (Table 2). Most strikingly, those who required a readmission were more likely than those who did not to be infected with Bacteroides spp, Candida spp, an MDR or an ESBL organism (Table 2). As for the outcomes of the index hospitalization, those with a repeat admission had a longer overall and postonset of sepsis initial hospital length of stay, and were less likely to be discharged either home without home health care or transferred to another hospital at the end of their index hospitalization (Table 3).
| 30‐Day Readmission = Yes | 30‐Day Readmission = No | P Value | |||
|---|---|---|---|---|---|
| N | % | N | % | ||
| |||||
| 543 | 32.00% | 1,154 | 68.00% | ||
| Gram‐positive BSI | 260 | 47.88% | 580 | 50.26% | 0.376 |
| Staphylococcus aureus | 138 | 25.41% | 287 | 24.87% | 0.810 |
| MRSA | 78 | 14.36% | 147 | 12.74% | 0.358 |
| VISA | 6 | 1.10% | 9 | 0.78% | 0.580 |
| Streptococcus pneumoniae | 7 | 1.29% | 33 | 2.86% | 0.058 |
| Streptococcus spp | 34 | 6.26% | 81 | 7.02% | 0.606 |
| Peptostreptococcus spp | 5 | 0.92% | 15 | 1.30% | 0.633 |
| Clostridium perfringens | 4 | 0.74% | 10 | 0.87% | 1.000 |
| Enterococcus faecalis | 54 | 9.94% | 108 | 9.36% | 0.732 |
| Enterococcus faecium | 29 | 5.34% | 63 | 5.46% | 1.000 |
| VRE | 36 | 6.63% | 70 | 6.07% | 0.668 |
| Gram‐negative BSI | 231 | 42.54% | 515 | 44.63% | 0.419 |
| Escherichia coli | 54 | 9.94% | 151 | 13.08% | 0.067 |
| Klebsiella pneumoniae | 54 | 9.94% | 108 | 9.36% | 0.723 |
| Klebsiella oxytoca | 11 | 2.03% | 18 | 1.56% | 0.548 |
| Enterobacter aerogenes | 6 | 1.10% | 13 | 1.13% | 1.000 |
| Enterobacter cloacae | 21 | 3.87% | 44 | 3.81% | 1.000 |
| Pseudomonas aeruginosa | 28 | 5.16% | 65 | 5.63% | 0.733 |
| Acinetobacter spp | 8 | 1.47% | 27 | 2.34% | 0.276 |
| Bacteroides spp | 25 | 4.60% | 30 | 2.60% | 0.039 |
| Serratia marcescens | 14 | 2.58% | 21 | 1.82% | 0.360 |
| Stenotrophomonas maltophilia | 3 | 0.55% | 8 | 0.69% | 1.000 |
| Achromobacter spp | 2 | 0.37% | 3 | 0.17% | 0.597 |
| Aeromonas spp | 2 | 0.37% | 1 | 0.09% | 0.241 |
| Burkholderia cepacia | 0 | 0.00% | 6 | 0.52% | 0.186 |
| Citrobacter freundii | 2 | 0.37% | 15 | 1.39% | 0.073 |
| Fusobacterium spp | 7 | 1.29% | 10 | 0.87% | 0.438 |
| Haemophilus influenzae | 1 | 0.18% | 4 | 0.35% | 1.000 |
| Prevotella spp | 1 | 0.18% | 6 | 0.52% | 0.441 |
| Proteus mirabilis | 9 | 1.66% | 39 | 3.38% | 0.058 |
| MDR PA | 2 | 0.37% | 7 | 0.61% | 0.727 |
| ESBL | 10 | 6.25% | 8 | 2.06% | 0.017 |
| CRE | 2 | 1.25% | 0 | 0.00% | 0.028 |
| MDR Gram‐negative or Gram‐positive | 231 | 47.53% | 450 | 41.86% | 0.036 |
| Candida spp | 58 | 10.68% | 76 | 6.59% | 0.004 |
| Polymicrobal BSI | 50 | 9.21% | 111 | 9.62% | 0.788 |
| Initially inappropriate treatment | 119 | 21.92% | 207 | 17.94% | 0.052 |
| 30‐Day Readmission = Yes | 30‐Day Readmission = No | ||||
|---|---|---|---|---|---|
| N = 543 | % = 32.00% | N = 1,154 | % = 68.00% | P Value | |
| |||||
| Hospital LOS, days | |||||
| Mean SD | 26.44 23.27 | 23.58 21.79 | 0.019 | ||
| Median (25, 75) | 19.16 (9.66, 35.86) | 17.77 (8.9, 30.69) | |||
| Hospital LOS following BSI onset, days | |||||
| Mean SD | 19.80 18.54 | 17.69 17.08 | 0.022 | ||
| Median (25, 75) | 13.9 (7.9, 25.39) | 12.66 (7.05, 22.66) | |||
| Discharge destination | |||||
| Home | 125 | 23.02% | 334 | 28.94% | 0.010 |
| Home with home care | 163 | 30.02% | 303 | 26.26% | 0.105 |
| Rehab | 81 | 14.92% | 149 | 12.91% | 0.260 |
| LTAC | 41 | 7.55% | 87 | 7.54% | 0.993 |
| Transfer to another hospital | 1 | 0.18% | 19 | 1.65% | 0.007 |
| SNF | 132 | 24.31% | 262 | 22.70% | 0.465 |
In a logistic regression model, 5 factors emerged as predictors of 30‐day readmission (Table 4). Having RIFLE: Injury or RIFLE: Failure carried an approximately 2‐fold increase in the odds of 30‐day rehospitalization (odds ratio: 1.95, 95% confidence interval: 1.302.93, P = 0.001) relative to having a RIFLE: None or RIFLE: Risk. Although having strong association with this outcome, harboring an ESBL organism or Bacteroides spp were both relatively infrequent events (3.3% ESBL and 3.2% Bacteroides spp). Infection with Escherichia coli and urine as the source of sepsis both appeared to be significantly protective against a readmission (Table 4). The model's discrimination was moderate (AUROC = 0.653) and its calibration adequate (Hosmer‐Lemeshow P = 0.907). (See Supporting Information, Appendix 1, in the online version of this article for the steps in the development of the final model.)
| OR | 95% CI | P Value | |
|---|---|---|---|
| |||
| ESBL | 4.503 | 1.42914.190 | 0.010 |
| RIFLE: Injury or Failure (reference: RIFLE: None or Risk) | 1.951 | 1.2972.933 | 0.001 |
| Bacteroides spp | 2.044 | 1.0583.948 | 0.033 |
| Source: urine | 0.583 | 0.3470.979 | 0.041 |
| Escherichia coli | 0.494 | 0.2700.904 | 0.022 |
DISCUSSION
In this single‐center retrospective cohort study, nearly one‐third of survivors of culture‐positive severe sepsis or septic shock required a rehospitalization within 30 days of discharge from their index admission. Factors that contributed to a higher odds of rehospitalization were having mild‐to‐moderate AKI (RIFLE: Injury or RIFLE: Failure) and infection with ESBL organisms or Bacteroides spp, whereas urine as the source of sepsis and E coli as the pathogen appeared to be protective.
A recent study by Hua and colleagues examining the New York Statewide Planning and Research Cooperative System for the years 2008 to 2010 noted a 16.2% overall rate of 30‐day rehospitalization among survivors of initial critical illness.[11] Just as we observed, Hua et al. concluded that development of AKI correlated with readmission. Because they relied on administrative data for their analysis, AKI was diagnosed when hemodialysis was utilized. Examining AKI using SCr changes, our findings add a layer of granularity to the relationship between AKI stages and early readmission. Specifically, we failed to detect any rise in the odds of rehospitalization when either very mild (RIFLE: Risk) or severe (RIFLE: Loss or RIFLE: ESKD) AKI was present. Only when either RIFLE: Injury or RIFLE: Failure developed did the odds of readmission rise. In addition to diverging definitions between our studies, differences in populations also likely yielded different results.[11] Although Hua et al. examined all admissions to the ICU regardless of the diagnosis or illness severity, our cohort consisted of only those ICU patients who survived culture‐positive severe sepsis/septic shock. Because AKI is a known risk factor for mortality in sepsis,[19] the potential for immortal time bias leaves a smaller pool of surviving patients with ESKD at risk for readmission. Regardless of the explanation, it may be prudent to focus on preventing AKI not only to improve survival, but also from the standpoint of diminishing the risk of an early readmission.
Four additional studies have examined the frequency of early readmissions among survivors of critical illness. Liu et al. noted 17.9% 30‐day rehospitalization rate among sepsis survivors.[12] Factors associated with the risk of early readmission included acute and chronic diseases burdens, index hospital LOS, and the need for the ICU in the index sepsis admission. In contrast to our cohort, all of whom were in the ICU during their index episode, less than two‐thirds of the entire population studied by Liu had required an ICU admission. Additionally, Liu's study did not specifically examine the potential impact of AKI or of microbiology on this outcome.
Prescott and coworkers examined healthcare utilization following an episode of severe sepsis.[13] Among other findings, they reported a 30‐day readmission rate of 26.5% among survivors. Although closer to our estimate, this study included all patients surviving a severe sepsis hospitalization, and not only those with a positive culture. These investigators did not examine predictors of readmission.[13]
Horkan et al. examined specifically whether there was an association between AKI and postdischarge outcomes, including 30‐day readmission risk, in a large cohort of patients who survived their critical illness.[20] In it they found that readmission risk ranged from 19% to 21%, depending on the extent of the AKI. Moreover, similar to our findings, they reported that in an adjusted analysis RIFLE: Injury and RIFLE: Failure were associated with a rise in the odds of a 30‐day rehospitalizaiton. In contrast to our study, Horkan et al. did detect an increase in the odds of this outcome associated with RIFLE: Risk. There are likely at least 3 reasons for this difference. First, we focused only on patients with severe sepsis or septic shock, whereas Horkan and colleagues included all critical illness survivors. Second, we were able to explore the impact of microbiology on this outcome. Third, Horkan's study included an order of magnitude more patients than did ours, thus making it more likely either to have the power to detect a true association that we may have lacked or to be more susceptible to type I error.
Finally, Goodwin and colleagues utilized 3 states' databases included in the Health Care and Utilization Project (HCUP) from the Agency for Healthcare Research and Quality to study frequency and risk factors for 30‐day readmission among survivors of severe sepsis.[21] Patients were identified based on the use of the severe sepsis (995.92) and septic shock (785.52). These authors found a 30‐day readmission rate of 26%. Although chronic renal disease, among several other factors, was associated with an increase in this risk, the data source did not permit these investigators to examine the impact of AKI on the outcomes. Similarly, HCUP data do not contain microbiology, a distinct difference from our analysis.
If clinicians are to pursue strategies to reduce the risk of an all‐cause 30‐day readmission, the key goal is not simply to identify all variables associated with readmission, but to focus on factors that are potentially modifiable. Although neither Hua nor Liu and their teams identified any additional factors that are potentially modifiable,[11, 12] in the present study, among the 5 factors we identified, the development of mild to moderate AKI during the index hospitalization may deserve stronger consideration for efforts at prevention. Although one cannot conclude automatically that preventing AKI in this population could mitigate some of the early rehospitalization risk, critically ill patients are frequently exposed to a multitude of nephrotoxic agents. Those caring for subjects with sepsis should reevaluate the risk‐benefit equation of these factors more cautiously and apply guideline‐recommended AKI prevention strategies more aggressively, particularly because a relatively minor change in SCr resulted in an excess risk of readmission.[22]
In addition to AKI, which is potentially modifiable, we identified several other clinical factors predictive of 30‐day readmission, which are admittedly not preventable. Thus, microbiology was predictive of this outcome, with E coli engendering fewer and Bacteroides spp and ESBL organisms more early rehospitalizations. Similarly, urine as the source of sepsis was associated with a lower risk for this endpoint.
Our study has a number of limitations. As a retrospective cohort, it is subject to bias, most notably a selection bias. Specifically, because the flagship hospital of the BJC HealthCare system is a referral center, it is possible that we did not capture all readmissions. However, generally, if a patient who receives healthcare within 1 of the BJC hospitals presents to a nonsystem hospital, that patient is nearly always transferred back into the integrated system because of issues of insurance coverage. Analysis of certain diagnosis‐related groups has indicated that 73% of all patients overall discharged from 4 of the large BJC system institutions who require a readmission within 30 days of discharge return to a BJC hospital (personal communication, Financial Analysis and Decision Support Department at BJC to Dr. Kollef May 12, 2015). Therefore, we may have misclassified the outcome in as many as 180 patients. The fact that our readmission rate was fully double that seen in Hua et al.'s and Liu et al.'s studies, and somewhat higher than that reported by Prescott et al., attests not only to the population differences, but also to the fact that we are unlikely to have missed a substantial percentage of readmissions.[11, 12, 13] Furthermore, to mitigate biases, we enrolled all consecutive patients meeting the predetermined criteria. Missing from our analysis are events that occurred between the index discharge and the readmission. Likewise, we were unable to obtain such potentially important variables as code status or outpatient mortality following discharge. These intervening factors, if included in subsequent studies, may increase the predictive power of the model. Because we relied on administrative coding to identify cases of severe sepsis and septic shock, it is possible that there is misclassification within our cohort. Recent studies indicate, however, that the Angus definition, used in our study, has high negative and positive predictive values for severe sepsis identification.[23] It is still possible that our cohort is skewed toward a more severely ill population, making our results less generalizable to the less severely ill septic patients.[24] The study was performed at a single healthcare system and included only cases of severe sepsis or septic shock that had a positive blood culture, and thus the findings may not be broadly generalizable either to patients without a positive blood culture or to institutions that do not resemble it.
In summary, we have demonstrated that survivors of culture‐positive severe sepsis or septic shock have a high rate of 30‐day rehospitalization. Because the US federal government's initiatives deem 30‐day readmissions to be a quality metric and penalize institutions with higher‐than average readmission rates, a high volume of critically ill patients with culture‐positive severe sepsis and septic shock may disproportionately put an institution at risk for such penalties. Unfortunately, not many of the determinants of readmission are amenable to prevention. As sepsis survival continues to improve, hospitals will need to concentrate their resources on coordinating care of these complex patients so as to improve both individual quality of life and the quality of care that they provide.
Disclosures
This study was supported by a research grant from Cubist Pharmaceuticals, Lexington, Massachusetts. Dr. Kollef's time was in part supported by the Barnes‐Jewish Hospital Foundation. The authors report no conflicts of interest.
- , , , et al; Sepsis Occurrence in Acutely Ill Patients Investigators. Sepsis in European intensive care units: results of the SOAP study. Crit Care Med. 2006;34:344–353
- , , , et al. Death in the United States, 2007. NCHS Data Brief. 2009;26:1–8.
- , , , et al. The epidemiology of sepsis in the United States from 1979 through 2000. N Engl J Med. 2003;348:1548–1564.
- , , , , , . Epidemiology of severe sepsis in the United States: analysis of incidence, outcome, and associated costs of care. Crit Care Med. 2001;29:1303–1310.
- , , , et al: Hospitalizations, costs, and outcomes of severe sepsis in the United States 2003 to 2007. Crit Care Med. 2012;40:754–761.
- , , , et al. Facing the challenge: decreasing case fatality rates in severe sepsis despite increasing hospitalization. Crit Care Med. 2005;33:2555–2562.
- , , , et al. Rapid increase in hospitalization and mortality rates for severe sepsis in the United States: a trend analysis from 1993 to 2003. Crit Care Med. 2007;35:1244–1250.
- , , , , . Two decades of mortality trends among patients with severe sepsis: a comparative meta‐analysis. Crit Care Med. 2014;42:625–631.
- , , , et al. Preventing 30‐day hospital readmissions: a systematic review and meta‐analysis of randomized trials. JAMA Intern Med. 2014;174:1095–1107.
- , . Trends in septicemia hospitalizations and readmissions in selected HCUP states, 2005 and 2010. HCUP Statistical Brief #161. Agency for Healthcare Research and Quality, Rockville, MD. Available at: http://www.hcup‐us.ahrq.gov/reports/statbriefs/sb161.pdf. Published September 2013, Accessed January 13, 2015.
- , , , . Early and late unplanned rehospitalizations for survivors of critical illness. Crit Care Med. 2015;43:430–438.
- , , , , , . Hospital readmission and healthcare utilization following sepsis in community settings. J Hosp Med. 2014;9:502–507.
- , , , , . Increased 1‐year healthcare use in survivors of severe sepsis. Am J Respir Crit Care Med. 2014;190:62–69.
- , , , , . Multi‐drug resistance, inappropriate initial antibiotic therapy and mortality in Gram‐negative severe sepsis and septic shock: a retrospective cohort study. Crit Care. 2014;18:596.
- , , . Does combination antimicrobial therapy reduce mortality in Gram‐negative bacteraemia? A meta‐analysis. Lancet Infect Dis. 2004;4:519–527.
- , , , et al. Multidrug‐resistant, extensively drug‐resistant and pandrug‐resistant bacteria: an international expert proposal for interim standard definitions for acquired resistance. Clin Microbiol Infect. 2012;18:268–281.
- , , , , ; Acute Dialysis Quality Initiative Workgroup. Acute renal failure—definition, outcome measures, animal models, fluid therapy and information technology needs: the Second International Consensus Conference of the Acute Dialysis Quality Initiative (ADQI) Group. Crit Care. 2004;8:R204–R212.
- , , , . APACHE II: a severity of disease classification system. Crit Care Med. 1985;13:818–829.
- , , , et al. RIFLE criteria for acute kidney injury are associated with hospital mortality in critically ill patients: a cohort analysis. Crit Care. 2006;10:R73
- , , , , , . The association of acute kidney injury in the critically ill and postdischarge outcomes: a cohort study. Crit Care Med. 2015;43:354–364.
- , , , . Frequency, cost, and risk factors of readmissions among severe sepsis survivors. Crit Care Med. 2015;43:738–746.
- Acute Kidney Injury Work Group. Kidney disease: improving global outcomes (KDIGO). KDIGO clinical practice guideline for acute kidney injury. Kidney Int Suppl. 2012;2:1–138. Available at: http://www.kdigo.org/clinical_practice_guidelines/pdf/KDIGO%20AKI%20Guideline.pdf. Accessed March 4, 2015.
- , , , , , . Validity of administrative data in recording sepsis: a systematic review. Crit Care. 2015;19(1):139.
- , , , , , . Severe sepsis cohorts derived from claims‐based strategies appear to be biased toward a more severely ill patient population. Crit Care Med. 2013;41:945–953.
- , , , et al; Sepsis Occurrence in Acutely Ill Patients Investigators. Sepsis in European intensive care units: results of the SOAP study. Crit Care Med. 2006;34:344–353
- , , , et al. Death in the United States, 2007. NCHS Data Brief. 2009;26:1–8.
- , , , et al. The epidemiology of sepsis in the United States from 1979 through 2000. N Engl J Med. 2003;348:1548–1564.
- , , , , , . Epidemiology of severe sepsis in the United States: analysis of incidence, outcome, and associated costs of care. Crit Care Med. 2001;29:1303–1310.
- , , , et al: Hospitalizations, costs, and outcomes of severe sepsis in the United States 2003 to 2007. Crit Care Med. 2012;40:754–761.
- , , , et al. Facing the challenge: decreasing case fatality rates in severe sepsis despite increasing hospitalization. Crit Care Med. 2005;33:2555–2562.
- , , , et al. Rapid increase in hospitalization and mortality rates for severe sepsis in the United States: a trend analysis from 1993 to 2003. Crit Care Med. 2007;35:1244–1250.
- , , , , . Two decades of mortality trends among patients with severe sepsis: a comparative meta‐analysis. Crit Care Med. 2014;42:625–631.
- , , , et al. Preventing 30‐day hospital readmissions: a systematic review and meta‐analysis of randomized trials. JAMA Intern Med. 2014;174:1095–1107.
- , . Trends in septicemia hospitalizations and readmissions in selected HCUP states, 2005 and 2010. HCUP Statistical Brief #161. Agency for Healthcare Research and Quality, Rockville, MD. Available at: http://www.hcup‐us.ahrq.gov/reports/statbriefs/sb161.pdf. Published September 2013, Accessed January 13, 2015.
- , , , . Early and late unplanned rehospitalizations for survivors of critical illness. Crit Care Med. 2015;43:430–438.
- , , , , , . Hospital readmission and healthcare utilization following sepsis in community settings. J Hosp Med. 2014;9:502–507.
- , , , , . Increased 1‐year healthcare use in survivors of severe sepsis. Am J Respir Crit Care Med. 2014;190:62–69.
- , , , , . Multi‐drug resistance, inappropriate initial antibiotic therapy and mortality in Gram‐negative severe sepsis and septic shock: a retrospective cohort study. Crit Care. 2014;18:596.
- , , . Does combination antimicrobial therapy reduce mortality in Gram‐negative bacteraemia? A meta‐analysis. Lancet Infect Dis. 2004;4:519–527.
- , , , et al. Multidrug‐resistant, extensively drug‐resistant and pandrug‐resistant bacteria: an international expert proposal for interim standard definitions for acquired resistance. Clin Microbiol Infect. 2012;18:268–281.
- , , , , ; Acute Dialysis Quality Initiative Workgroup. Acute renal failure—definition, outcome measures, animal models, fluid therapy and information technology needs: the Second International Consensus Conference of the Acute Dialysis Quality Initiative (ADQI) Group. Crit Care. 2004;8:R204–R212.
- , , , . APACHE II: a severity of disease classification system. Crit Care Med. 1985;13:818–829.
- , , , et al. RIFLE criteria for acute kidney injury are associated with hospital mortality in critically ill patients: a cohort analysis. Crit Care. 2006;10:R73
- , , , , , . The association of acute kidney injury in the critically ill and postdischarge outcomes: a cohort study. Crit Care Med. 2015;43:354–364.
- , , , . Frequency, cost, and risk factors of readmissions among severe sepsis survivors. Crit Care Med. 2015;43:738–746.
- Acute Kidney Injury Work Group. Kidney disease: improving global outcomes (KDIGO). KDIGO clinical practice guideline for acute kidney injury. Kidney Int Suppl. 2012;2:1–138. Available at: http://www.kdigo.org/clinical_practice_guidelines/pdf/KDIGO%20AKI%20Guideline.pdf. Accessed March 4, 2015.
- , , , , , . Validity of administrative data in recording sepsis: a systematic review. Crit Care. 2015;19(1):139.
- , , , , , . Severe sepsis cohorts derived from claims‐based strategies appear to be biased toward a more severely ill patient population. Crit Care Med. 2013;41:945–953.
© 2015 Society of Hospital Medicine
Predicting Recurrence Risk
Clostridium difficile infection (CDI) is a serious and costly condition whose volume in US hospitals has doubled over the last decade.[1, 2, 3] Along with this rise in incidence, its severity has also increased. Although in the United States there has been a doubling in age‐adjusted case fatality, in the same time period Canadian studies reported a high and increasing CDI‐associated case fatality in the setting of an outbreak of a novel epidemic hypervirulent strain BI/NAP1/027.[2, 4, 5, 6] The costs of CDI range widely ($2500 to $13,000 per hospitalization), with cumulative annual cost to the US healthcare system estimated at nearly $5 billion.[7, 8, 9]
One of the drivers of these clinical and economic outcomes is CDI recurrence (rCDI). In 2 recent randomized controlled trials, up to 25% of patients with an initial CDI (iCDI) episode developed rCDI.[10, 11] There are few data that quantify the impact of rCDI on quality of life and survival. However, patients often are readmitted to the hospital with rCDI, and physicians who treat patients with multiple episodes of rCDI can attest to the devastating toll it takes on the lives of the patients and their families (personal communications from numerous patients to E.R.D.).[12] Reducing the incidence of rCDI may significantly improve the course of this disease.
The advent of such new treatments as fidaxomicin aimed at rCDI is promising.[10, 11] However, evidence for its efficacy so far is limited to treatment‐naive iCDI patients, thus challenging clinicians to identify patients at high risk for rCDI at iCDI onset. To address this challenge, we set out to develop a bedside prediction model for rCDI based on the factors present and routinely available at the onset of iCDI.
METHODS
Study Design and Data Source
We conducted a retrospective single‐center cohort study to examine the factors present at the onset of iCDI that impact the incidence of rCDI among hospitalized patients. Patients were included in the study if they were adults (18 years) hospitalized at Barnes‐Jewish Hospital (BJH), St. Louis, Missouri, between January 1, 2003 and December 31, 2009, and who had a positive toxin assay for C difficile in the setting of unformed stools and no history of CDI in the previous 60 days (as defined by positive toxin assay). Patients were excluded if they either died during or were discharged to hospice from the iCDI hospitalization. Cases of iCDI were categorized according to published surveillance definitions as community onset‐healthcare facility associated (CO‐HCFA), healthcare facility onset, and community associated.[13] Notably, the CO‐HCFA category included surveillance definitions for both CO‐HCFA and indeterminate cases. We defined rCDI as a repeat positive toxin within 42 days following the end of iCDI treatment. This period of risk for rCDI was chosen because the current surveillance definition for rCDI is a new episode of CDI occurring within 8 weeks from the last episode of CDI, with the assumption the patient would receive 10 to 14 days of CDI treatment at the beginning of the 8‐week period.[14] Medical charts were reviewed for all readmissions during the recurrence risk period to identify patients diagnosed with rCDI by methods other than toxin assay. A study enrollment flow chart is shown in Figure 1.
Demographic and clinical data were derived from the BJH medical informatics databases and the BJH electronic medical records (see Supporting Appendix Table 1 in the online version of this article). Comorbidities were grouped using the Charlson‐Deyo categories.[15] All variables were limited to data that are consistent throughout a hospitalization (eg, race or age) or were present within 48 hours of iCDI (eg, medications).
Model Development and Validation
First, we examined risk factors for rCDI present at the time of the iCDI diagnosis and initiation of iCDI therapy. We used principal‐component analyses, corresponding analyses, and cluster analyses to reduce the data dimensions by combining variables reflecting the same underlying construct.[16] Several antibiotic categories were created. The high‐risk category included cephalosporins, clindamycin, and aminopenicillins.[17] Other categories examined separately were fluoroquinolones, intravenous vancomycin, and antibiotics considered low risk (all other drugs not encompassed in the prior categories). Proton pump inhibitor and histamine 2 receptor‐blockers were combined into a single variable of gastric acid suppressors.
We developed a logistic regression model to identify a set of variables that best predicted the risk of rCDI. Variables with P 0.20 on univariate analyses were included in multivariable models. Backward elimination was used to determine the final model (P 0.1 for removal). The model's discrimination was examined via the C statistic and calibration through Brier score.[16] A C statistic value of 0.5 implies that the model is no better than chance, whereas the value of 1.0 means that the model is perfect in differentiating cases from noncases. A Brier score closer to zero indicates better model calibration, or how closely the predicted probabilities for rCDI match the actual observed probabilities. We validated the model using the bootstrap method with 500 iterations. To explore its properties as a decision tool to help make the decision to initiate an intervention to prevent rCDI, we tested the model's sensitivity, specificity, and positive and negative predictive values at various thresholds of prior probability of rCDI.
RESULTS
Among the 4196 patients with iCDI enrolled in the study, 425 (10.1%) developed at least 1 recurrence within 42 days of the end of iCDI treatment (Table 1). Compared to patients without a recurrence, in univariate analysis those with an rCDI episode were older and had a greater comorbidity burden. In particular, diabetes mellitus (odds ratio 1.34; 95% confidence interval [CI], 1.08‐1.66) and cerebrovascular disease (odds ratio 1.47; 95% CI, 1.04‐2.08) were significantly more prevalent in the rCDI group. The index CDI episode for patients with rCDI was approximately twice as likely to fit the surveillance definition for CO‐HCFA than the index episode for those without a recurrence (odds ratio 2.24; 95% CI, 1.80‐2.79). Commensurately, patients with rCDI also had greater odds for experiencing multiple recent hospitalizations than those without rCDI. Neither type of CDI treatment (oral metronidazole vs oral vancomycin vs both), nor duration, was significantly associated with recurrence.
| Patient Characteristics | Patients Who Developed rCDI, N = 425 | Patients Who Did Not Develop rCDI, n = 3771) | Odds Ratio (95% CI) | P Value |
|---|---|---|---|---|
| ||||
| Demographics | ||||
| Age, y, median (range)a | 64.8(18.398.2) | 61.6(18.0102.4) | 1.10 (1.041.16) | <0.001 |
| Female | 210 (49) | 1822 (48) | 1.05 (0.861.28) | 0.67 |
| Nonwhite race | 149 (35) | 1149 (31) | 1.23 (1.001.52) | 0.05 |
| Comorbiditiesb | ||||
| Myocardial infarction | 40 (9) | 328 (9) | 1.10 (0.771.54) | 0.62 |
| Congestive heart failure | 108 (25) | 854 (23) | 1.17 (0.931.47) | 0.19 |
| Peripheral vascular disease | 34 (8) | 269 (7) | 1.13 (0.781.64) | 0.51 |
| Cerebrovascular disease | 41 (10) | 256 (7) | 1.47 (1.042.08) | 0.03 |
| Chronic renal failure | 21 (5) | 190 (5) | 0.98 (0.621.56) | 0.94 |
| Dementia | 5 (1) | 23 (1) | 1.94 (0.735.14) | 0.18 |
| Chronic obstructive pulmonary disease | 116 (27) | 911 (24) | 1.18 (0.941.48) | 0.15 |
| Rheumatologic disease | 18 (4) | 146 (4) | 1.10 (0.671.81) | 0.71 |
| Peptic ulcer disease | 20 (5) | 154 (4) | 1.16 (0.721.87) | 0.54 |
| Mild liver disease | 17 (4) | 201 (5) | 0.74 (0.451.23) | 0.25 |
| Moderate‐to‐severe liver disease | 12 (3) | 134 (4) | 0.79 (0.431.44) | 0.44 |
| Diabetes, any | 135 (32) | 974 (26) | 1.34 (1.081.66) | 0.009 |
| Paraplegia or hemiplegia | 12 (3) | 77 (2) | 1.38 (0.742.55) | 0.31 |
| Any malignancy (excluding leukemia/lymphoma) | 83 (20) | 770 (20) | 0.95 (0.741.22) | 0.67 |
| Leukemia or lymphoma | 78 (18) | 660 (18) | 1.06 (0.821.38) | 0.66 |
| Metastatic solid tumor | 56 (13) | 449 (12) | 1.12 (0.841.51) | 0.44 |
| HIV/AIDS | 10 (2) | 66 (2) | 1.36 (0.692.67) | 0.38 |
| Charlson composite score | ||||
| 02 | 223 (53) | 2179 (58) | Ref | |
| 35 | 117 (28) | 921 (24) | 1.24 (0.981.57) | 0.07 |
| 6 | 85(20) | 671 (18) | 1.24 (0.951.61) | 0.11 |
| Case statusc | ||||
| HCFO/HCFA | 203 (48) | 2331 (62) | Ref | |
| CA or unknown | 57 (13) | 595 (16) | 1.10 (0.811.50) | 0.54 |
| CO/HCFA, indeterminate, or non‐ BJHHCFA | 165 (39) | 845 (22) | 2.24 (1.802.79) | <0.001 |
| Prior hospitalizations | ||||
| Admitted from another healthcare facility | 109 (26) | 1018 (27) | 0.93 (0.741.17) | 0.55 |
| No. of inpatient admissions in previous 60 days | <0.001 | |||
| 0 | 200 (47) | 2310 (61) | Ref | |
| 1 | 150 (35) | 1020 (27) | 1.70 (1.362.13) | <0.001 |
| 2+ | 75 (18) | 441 (12) | 1.96 (1.482.61) | <0.001 |
| Baseline laboratory datad | ||||
| Low albumin at iCDI | 50 (12) | 548 (15) | 0.78 (0.581.07) | 0.312 |
| Low WBC at iCDI | 64 (15) | 635 (17) | 0.88 (0.661.16) | 0.36 |
| High WBC at iCDI | 247 (58) | 2027 (54) | 1.20 (0.981.46) | 0.08 |
| Low hemoglobin at iCDI | 218 (51) | 1985 (53) | 0.95 (0.781.16) | 0.61 |
| High creatinine at iCDI | 99 (23) | 862 (23) | 1.02 (0.811.30) | 0.83 |
| Low creatinine clearance at iCDI | 218 (51) | 1635 (43) | 1.38 (1.131.68) | 0.002 |
| ICU admission at iCDI | 32 (8) | 562 (15) | 0.47 (0.320.68) | <0.001 |
| Medications | ||||
| New gastric acid suppressor at iCDI | 54 (13) | 255 (7) | 2.01 (1.472.74) | <0.001 |
| Any antibiotic at iCDI | 314 (74) | 2727 (72) | 1.08 (0.861.36) | 0.49 |
| High‐risk antibiotics at iCDIe | 174 (41) | 1489 (40) | 1.06 (0.871.30) | 0.56 |
| Fluoroquinolone at iCDI | 120 (28) | 860 (23) | 1.33 (1.061.67) | 0.01 |
| Low‐risk antibiotics at iCDI | 95 (22) | 1058 (28) | 0.74 (0.580.94) | 0.01 |
| IV vancomycin at iCDI | 130 (31) | 1321 (35) | 0.82 (0.671.02) | 0.07 |
Seven factors present at the onset of iCDI were found to predict a recurrence in multivariable analysis (Table 2). Older age, CO‐HCFA status of iCDI, and 2 or more hospitalizations in the prior 60 days increased the risk of rCDI. Concomitant exposures to gastric acid suppressors, fluoroquinolone antibiotics, and high‐risk antibiotics were also significantly associated with a recurrence. Being in the intensive care unit (ICU) at the onset of iCDI was protective against rCDI in the multivariable model. This model had a C statistic of 0.642 and a Brier score of 0.089. After cross‐validation with 500 bootstrapping iterations, the model exhibited a moderately good fit (Figure 2). The prediction was particularly accurate in the lower risk ranges, with slight divergence in the risk strata over 20%. The validated model had a C statistic of 0.630 and Brier score of 0.089.
| Prediction Factors | Adjusted Odds Ratio | 95% CI |
|---|---|---|
| ||
| Agea | 1.08 | 1.021.14 |
| CO‐HCFA CDI (ref: HO‐CDI) | 1.71 | 1.322.22 |
| 2+ hospitalizations in prior 60 days (ref: 0 hospitalizations) | 1.49 | 1.082.06 |
| New gastric acid suppression at the onset of iCDI | 1.59 | 1.132.23 |
| High‐risk antibiotic at the onset of iCDIbb | 1.25 | 1.011.55 |
| Fluoroquinolone at the onset of iCDI | 1.31 | 1.041.65 |
| ICU at the onset of iCDI | 0.49 | 0.340.72 |
The sensitivity, specificity, and positive and negative predictive values of the model at various probability thresholds of rCDI are presented in Table 3. Thus, when the probability of rCDI was low, the model exhibited high sensitivity and low specificity. The situation was reversed as the probability of rCDI approached 30% (very low sensitivity and high specificity). The model's performance was optimal when the rCDI risk matched that in the current cohort, or 10.1%, with a sensitivity of 56% and specificity of 65%. However, when the rCDI risk dropped to 5%, the specificity dropped to below 30%. The sensitivity dropped to below 30% when rCDI risk rose to 15% (Table 3). Across the entire range of the probabilities tested, the negative predictive value of the model was persistently 90% or higher.
| Model Predicted Probability Cutpoint | Sensitivity | Specificity | PPV | NPV | Positive Likelihood Ratio | Negative Likelihood Ratio |
|---|---|---|---|---|---|---|
| ||||||
| 0.025 | 1.00 | 0.00 | 0.10 | 1.00 | 1.0 | Undef |
| 0.050 | 0.96 | 0.09 | 0.11 | 0.95 | 1.05 | 0.44 |
| 0.101a | 0.56 | 0.65 | 0.15 | 0.93 | 1.60 | 0.68 |
| 0.151 | 0.27 | 0.86 | 0.18 | 0.91 | 1.93 | 0.85 |
| 0.303 | 0.01 | 1.00 | 0.40 | 0.90 | Undef | 0.99 |
DISCUSSION
We have demonstrated that in a cohort of hospitalized patients with iCDI, 10% developed at least 1 episode of rCDI within 42 days of the end of iCDI treatment. The factors present at iCDI onset that predicted recurrence were age, CO‐HCFA CDI, prior hospitalization, high‐risk antibiotic and fluoroquinolone use, and gastric acid suppression. Although the model's performance was only moderate, its negative predictive value was 90% or higher across the entire range of rCDI probabilities tested. This means that the absence of this combination of risk factors in a patient with iCDI diminishes the probability of a rCDI episode to 10% or below, depending on the prior population risk for rCDI.
Prior investigators have developed prediction rules for rCDI. Hebert et al., using methodology similar to ours, constructed a model to predict the risk of rCDI among patients hospitalized with iCDI.[17] For example, the recurrence rate in their study was 23% compared to our 10%. This is likely due to the differing definitions of both iCDI and rCDI between the 2 studies. Although our definitions of hospital‐associated C difficile‐associated diarrhea (CDAD) conformed to the recommended surveillance definitions,[13] Hebert and colleagues used different definitions.[18] If this is so, the higher rate of rCDI in their study may have reflected these differences in surveillance definition, rather than the true prevalence of recurrent CDAD.
Several other studies have relied on either specialized laboratory tests alone or in combination with clinical factors. Stewart et al., in a small single‐center cohort study, reported the presence of the binary toxin to be the only independent predictor of rCDI.[19] Others have found lower antitoxin immunoglobulin levels at various times following the onset of iCDI to be predictive of a recurrence.[20, 21] A disadvantage of using these specialized tests as tools for clinical prediction is that they are not widely available in clinical practice. Even if these tests are available, their results are likely to return only after iCDI treatment has commenced. To make risk stratification more generalizable, we specifically focused on common data available in all clinical settings at the onset of iCDI.
We chose to restrict our risk stratification to factors present at the onset of iCDI for several reasons. First, earlier identification of patients at increased risk for rCDI may encourage clinicians to minimize subsequent exposures to non‐CDI antimicrobials and gastric acid suppressors. Second, newer anticlostridial therapies in development appear to target specifically CDI recurrence. The first anti‐CDI drug to be approved in 2 decades, fidaxomicin, has been shown to reduce the risk of a recurrence by nearly one‐half compared to vancomycin.[10, 11] Although in practice it is tempting to reserve this treatment for those patients who have multiple recurrences, there is no convincing evidence to date that the drug is similarly effective at reducing further recurrences in this population.[22, 23] Currently, the only population in which fidaxomicin treatment has been shown to reduce the risk of rCDI contains patients with at most 1 prior episode, whose first anti‐CDI exposure was to fidaxomicin.[10, 11] Thus, the intent of our model was to insure appropriate use of these new technologies from the perspective of both under‐ and overtreatment.
In general, most of the factors included in our model are neither novel nor surprising, including concurrent antibiotics and gastric acid suppression.[24, 25, 26, 27, 28, 29, 30] What is interesting about these exposures, however, is the fact that we measured them only at the onset of the iCDI episode. This implies that it is not merely the continuation of these medications after onset, but even exposure to them prior to the initial bout of CDI, that may promote a recurrence. This finding should give pause to the widespread practice of routinely prescribing gastric acid suppression to many hospitalized patients. It should also prompt a reexamination of antimicrobial choices for patients admitted for the treatment of infectious diseases in favor of those deemed at low risk for CDI whenever possible.
A relatively novel risk factor emerging from our model is the designation of the iCDI episode as CO‐HCFA.[30] A likely explanation for this relationship is that CO‐HCFA identifies a population of patients who are more ill, as evidenced by their prior hospitalization history. However, because recent hospitalizations themselves emerged as an independent predictor of rCDI in our model, CO‐HCFA designation clearly incorporates other factors important to this outcome.
Our data on illness severity are divergent from prior results. Previous work has found that increasing severity of illness is positively associated with the risk of a recurrence.[21, 31] In contrast, we found that the need for the ICU at the onset of iCDI appeared protective from rCDI. There are several explanations for this finding, the most likely being the competing mortality risk. Although we excluded from the study those patients who did not survive their iCDI hospitalizations, patients who received care in an ICU were more likely to die in the rCDI risk period than patients who did not receive care in an ICU (data not shown). Another potential explanation for this observation is that patients who develop iCDI while in the ICU may generally get more aggressive care than those contracting it on other wards, resulting in a lower risk for recurrence.
The recurrence rate in the current study is at the lower limit of what has been reported previously either in the meta‐analysis by Garey (13%50%) or in recent randomized controlled trials (25%).[10, 11, 25] This is likely due to our case identification pathway, and ascertainment bias is a potential limitation of our study. Patients with mild recurrent CDI diagnosed and treated as outpatients were not captured in our study unless their toxin assay was performed by the BJH laboratory (approximately 15% of specimens submitted to the BJH microbiology laboratory come from outpatients or affiliated outpatient or skilled nursing facilities). Similarly, recurrences diagnosed at other inpatient facilities were not captured in our study unless they were transferred to BJH for care. On the other hand, rCDI in randomized trials may be subject to a detection bias, because enrolled patients are prospectively monitored for and instructed to seek testing for recurrent diarrhea.
Our study also has limitations inherent to observational data such as confounding. We adjusted for all the available relevant potential confounders in the regression model. However, the possibility of residual confounding remains. Because our cohort was too small for a split‐cohort model validation, we employed a bootstrap method to cross‐validate our results. However, the model requires further validation in a prospective cohort in the future. The biggest limitation of our model, however, is its generalizability, because the data reflect patients and treatment patterns at an urban academic medical center, and may not mirror those of institutions with different characteristics or patients with iCDI diagnosed and managed completely in the outpatient setting.
In summary, we have developed a model to predict iCDI patients' risk of recurrence. The advantage of our model is the availability of all the factors at the onset of iCDI, when treatment decisions need to be made. Although far from perfect in its ability to discriminate those who will from those who will not develop a recurrence, it should serve as a beginning step in the direction of appropriately aggressive care that may result not only in diminishing the pool of this infection, but also in containing its spiraling costs. The cost‐benefit balance of these decisions needs to be examined explicitly, not only in terms of the financial cost of over‐ or undertreatment, but with respect to the implications of such overtreatment on development of resistance to newer anticlostridial agents.
Disclosures
This study was funded by Cubist Pharmaceuticals, Jersey City, New Jersey. The data in the article were presented in part as a poster presentation at IDWeek 2012, San Diego, California, October 1721, 2012. The authors report no conflicts of interest.
- , , . Clostridium difficile infection in patients discharged from US short‐stay hospitals, 1996–2003. Emerge Infect Dis. 2006;12:409–415.
- , , . Increase in adult Clostridium difficile‐related hospitalizations and case‐fatality rate, United States, 2000–2005. Emerg Infect Dis. 2008;14:929–931.
- , , . Clostridium difficile infections (CDI) in hospital stays, 2009. HCUP statistical brief #124. Rockville, MD: Agency for Healthcare Research and Quality; 2012. Available at: http://www.hcup‐us.ahrq.gov/reports/statbriefs/sb124.pdf. Accessed July 19, 2013.
- , , , et al. A predominantly clonal multi‐institutional outbreak of Clostridium difficile‐associated diarrhea with high morbidity and mortality. N Engl J Med. 2005;353:2442–2449.
- , , , et al. Clostridium difficile‐associated diarrhea in a region of Quebec from 1991 to 2003: a changing pattern of disease severity. CMAJ. 2004;171:466–472.
- , , , et al. An epidemic, toxin gene‐variant strain of Clostridium difficile. N Engl J Med. 2005;353(23):2433–2441.
- , , , , . Short‐ and long‐term attributable costs of Clostridium difficile‐associated disease in nonsurgical inpatients. Clin Infect Dis. 2008;46(4):497–504.
- , , , . The emerging infectious challenge of Clostridium difficile‐associated disease in Massachusetts hospitals: clinical and economic consequences. Infect Control Hosp Epidemiol. 2007;28:1219–1227.
- , . Burden of Clostridium difficile on the healthcare system. Clin Infect Dis. 2012;55(suppl 2):S88–S92.
- , , , et al. Fidaxomicin versus vancomycin for infection with Clostridium difficile in Europe, Canada, and the USA: a double‐blind, non‐inferiority, randomised controlled trial. Lancet Infect Dis. 2012;12(4):281–289.
- , , , et al. Fidaxomicin versus vancomycin for Clostridium difficile infection. N Engl J Med. 2011;364:422–431.
- , , , , , . Recurrent Clostridium difficile disease: epidemiology and clinical characteristics. Infect Control Hosp Epidemiol. 1999;20:43–50.
- , , , et al. Clinical practice guidelines for Clostridium difficile infection in adults: 2010 update by the Society for Healthcare Epidemiology of America (SHEA) and the Infectious Diseases Society of America (IDSA). Infect Control Hosp Epidemiol. 2010;31:431–455.
- , , , , , ; Ad Hoc Clostridium difficile Surveillance Working Group. Recommendations for surveillance of Clostridium difficile‐associated disease. Infect Control Hosp Epidemiol. 2007;28:140–145.
- , , . Adapting a clinical comorbidity index for use with ICD‐9‐CM administrative databases. J Clin Epidemiol. 1992;45:613–619.
- , , , . Measures for evaluating model performance. Proceedings of the Biometrics Section. Alexandria, VA: American Statistical Association, Biometrics Section; 1997:253–258.
- , , , et al. Development and validation of a Clostridium difficile infection risk prediction model. Infect Control Hosp Epidemiol. 2011;32:360–366.
- , , , . Electronic health record‐based detection of risk factors for Clostridium difficile infection relapse. Infect Control Hosp Epidemiol. 2013;34:407–414.
- , , . Predicting recurrence of C. difficile colitis using bacterial virulence factors: binary toxin is the key. J Gastrointest Surg. 2013;17:118–125.
- , , , . Association between antibody response to toxin A and protection against recurrent Clostridium difficile diarrhoea. Lancet. 2001;357:189–193.
- , , , et al. Prospective derivation and validation of a clinical prediction rule for recurrent Clostridium difficile infection. Gastroenterology. 2009;136:1206–1214.
- . Fidaxomicin failures in recurrent Clostridium difficile infection: a problem of timing. Clin Infect Dis. 2012;55:613–614.
- , . Fidaxomicin “chaser” regimen following vancomycin for patients with multiple Clostridium difficile recurrences. Clin Infect Dis. 2013;56:309–310.
- , , , et al. Predictors of first recurrence of Clostridium difficile infection: implications for initial management. Clin Infect Dis. 2012;55(suppl 2):S77–S87.
- , , , . Meta‐analysis to assess risk factors for recurrent Clostridium difficile infection. J Hosp Infect. 2008;70:298–304.
- , , , , , . Recurrent Clostridium difficile diarrhea: characteristics of and risk factors for patients enrolled in a prospective, randomized, double‐blinded trial. Clin Infect Dis. 1997;24:324–333.
- , , , , . Association of proton‐pump inhibitors with outcomes in Clostridium difficile colitis. Am J Health Syst Pharm. 2007;64:2359–2363.
- , , , et al. Proton pump inhibitors as a risk factor for recurrence of Clostridium‐difficile‐associated diarrhea. World J Gastroenterol. 2010;16:3573–3577.
- , , . Proton pump inhibitor use and recurrent Clostridium difficile‐associated disease: a case‐control analysis matched by propensity score. J Clin Gastroenterol. 2012;46:397–400.
- , , , , , . Risk of Clostridium difficile infection with acid suppressing drugs and antibiotics: meta‐analysis. Am J Gastroenterol. 2012;107(7):1011–1019.
- , , , et al. Risk factors for early recurrent Clostridium difficile‐associated diarrhea. Clin Infect Dis. 1998;26:954–959.
Clostridium difficile infection (CDI) is a serious and costly condition whose volume in US hospitals has doubled over the last decade.[1, 2, 3] Along with this rise in incidence, its severity has also increased. Although in the United States there has been a doubling in age‐adjusted case fatality, in the same time period Canadian studies reported a high and increasing CDI‐associated case fatality in the setting of an outbreak of a novel epidemic hypervirulent strain BI/NAP1/027.[2, 4, 5, 6] The costs of CDI range widely ($2500 to $13,000 per hospitalization), with cumulative annual cost to the US healthcare system estimated at nearly $5 billion.[7, 8, 9]
One of the drivers of these clinical and economic outcomes is CDI recurrence (rCDI). In 2 recent randomized controlled trials, up to 25% of patients with an initial CDI (iCDI) episode developed rCDI.[10, 11] There are few data that quantify the impact of rCDI on quality of life and survival. However, patients often are readmitted to the hospital with rCDI, and physicians who treat patients with multiple episodes of rCDI can attest to the devastating toll it takes on the lives of the patients and their families (personal communications from numerous patients to E.R.D.).[12] Reducing the incidence of rCDI may significantly improve the course of this disease.
The advent of such new treatments as fidaxomicin aimed at rCDI is promising.[10, 11] However, evidence for its efficacy so far is limited to treatment‐naive iCDI patients, thus challenging clinicians to identify patients at high risk for rCDI at iCDI onset. To address this challenge, we set out to develop a bedside prediction model for rCDI based on the factors present and routinely available at the onset of iCDI.
METHODS
Study Design and Data Source
We conducted a retrospective single‐center cohort study to examine the factors present at the onset of iCDI that impact the incidence of rCDI among hospitalized patients. Patients were included in the study if they were adults (18 years) hospitalized at Barnes‐Jewish Hospital (BJH), St. Louis, Missouri, between January 1, 2003 and December 31, 2009, and who had a positive toxin assay for C difficile in the setting of unformed stools and no history of CDI in the previous 60 days (as defined by positive toxin assay). Patients were excluded if they either died during or were discharged to hospice from the iCDI hospitalization. Cases of iCDI were categorized according to published surveillance definitions as community onset‐healthcare facility associated (CO‐HCFA), healthcare facility onset, and community associated.[13] Notably, the CO‐HCFA category included surveillance definitions for both CO‐HCFA and indeterminate cases. We defined rCDI as a repeat positive toxin within 42 days following the end of iCDI treatment. This period of risk for rCDI was chosen because the current surveillance definition for rCDI is a new episode of CDI occurring within 8 weeks from the last episode of CDI, with the assumption the patient would receive 10 to 14 days of CDI treatment at the beginning of the 8‐week period.[14] Medical charts were reviewed for all readmissions during the recurrence risk period to identify patients diagnosed with rCDI by methods other than toxin assay. A study enrollment flow chart is shown in Figure 1.
Demographic and clinical data were derived from the BJH medical informatics databases and the BJH electronic medical records (see Supporting Appendix Table 1 in the online version of this article). Comorbidities were grouped using the Charlson‐Deyo categories.[15] All variables were limited to data that are consistent throughout a hospitalization (eg, race or age) or were present within 48 hours of iCDI (eg, medications).
Model Development and Validation
First, we examined risk factors for rCDI present at the time of the iCDI diagnosis and initiation of iCDI therapy. We used principal‐component analyses, corresponding analyses, and cluster analyses to reduce the data dimensions by combining variables reflecting the same underlying construct.[16] Several antibiotic categories were created. The high‐risk category included cephalosporins, clindamycin, and aminopenicillins.[17] Other categories examined separately were fluoroquinolones, intravenous vancomycin, and antibiotics considered low risk (all other drugs not encompassed in the prior categories). Proton pump inhibitor and histamine 2 receptor‐blockers were combined into a single variable of gastric acid suppressors.
We developed a logistic regression model to identify a set of variables that best predicted the risk of rCDI. Variables with P 0.20 on univariate analyses were included in multivariable models. Backward elimination was used to determine the final model (P 0.1 for removal). The model's discrimination was examined via the C statistic and calibration through Brier score.[16] A C statistic value of 0.5 implies that the model is no better than chance, whereas the value of 1.0 means that the model is perfect in differentiating cases from noncases. A Brier score closer to zero indicates better model calibration, or how closely the predicted probabilities for rCDI match the actual observed probabilities. We validated the model using the bootstrap method with 500 iterations. To explore its properties as a decision tool to help make the decision to initiate an intervention to prevent rCDI, we tested the model's sensitivity, specificity, and positive and negative predictive values at various thresholds of prior probability of rCDI.
RESULTS
Among the 4196 patients with iCDI enrolled in the study, 425 (10.1%) developed at least 1 recurrence within 42 days of the end of iCDI treatment (Table 1). Compared to patients without a recurrence, in univariate analysis those with an rCDI episode were older and had a greater comorbidity burden. In particular, diabetes mellitus (odds ratio 1.34; 95% confidence interval [CI], 1.08‐1.66) and cerebrovascular disease (odds ratio 1.47; 95% CI, 1.04‐2.08) were significantly more prevalent in the rCDI group. The index CDI episode for patients with rCDI was approximately twice as likely to fit the surveillance definition for CO‐HCFA than the index episode for those without a recurrence (odds ratio 2.24; 95% CI, 1.80‐2.79). Commensurately, patients with rCDI also had greater odds for experiencing multiple recent hospitalizations than those without rCDI. Neither type of CDI treatment (oral metronidazole vs oral vancomycin vs both), nor duration, was significantly associated with recurrence.
| Patient Characteristics | Patients Who Developed rCDI, N = 425 | Patients Who Did Not Develop rCDI, n = 3771) | Odds Ratio (95% CI) | P Value |
|---|---|---|---|---|
| ||||
| Demographics | ||||
| Age, y, median (range)a | 64.8(18.398.2) | 61.6(18.0102.4) | 1.10 (1.041.16) | <0.001 |
| Female | 210 (49) | 1822 (48) | 1.05 (0.861.28) | 0.67 |
| Nonwhite race | 149 (35) | 1149 (31) | 1.23 (1.001.52) | 0.05 |
| Comorbiditiesb | ||||
| Myocardial infarction | 40 (9) | 328 (9) | 1.10 (0.771.54) | 0.62 |
| Congestive heart failure | 108 (25) | 854 (23) | 1.17 (0.931.47) | 0.19 |
| Peripheral vascular disease | 34 (8) | 269 (7) | 1.13 (0.781.64) | 0.51 |
| Cerebrovascular disease | 41 (10) | 256 (7) | 1.47 (1.042.08) | 0.03 |
| Chronic renal failure | 21 (5) | 190 (5) | 0.98 (0.621.56) | 0.94 |
| Dementia | 5 (1) | 23 (1) | 1.94 (0.735.14) | 0.18 |
| Chronic obstructive pulmonary disease | 116 (27) | 911 (24) | 1.18 (0.941.48) | 0.15 |
| Rheumatologic disease | 18 (4) | 146 (4) | 1.10 (0.671.81) | 0.71 |
| Peptic ulcer disease | 20 (5) | 154 (4) | 1.16 (0.721.87) | 0.54 |
| Mild liver disease | 17 (4) | 201 (5) | 0.74 (0.451.23) | 0.25 |
| Moderate‐to‐severe liver disease | 12 (3) | 134 (4) | 0.79 (0.431.44) | 0.44 |
| Diabetes, any | 135 (32) | 974 (26) | 1.34 (1.081.66) | 0.009 |
| Paraplegia or hemiplegia | 12 (3) | 77 (2) | 1.38 (0.742.55) | 0.31 |
| Any malignancy (excluding leukemia/lymphoma) | 83 (20) | 770 (20) | 0.95 (0.741.22) | 0.67 |
| Leukemia or lymphoma | 78 (18) | 660 (18) | 1.06 (0.821.38) | 0.66 |
| Metastatic solid tumor | 56 (13) | 449 (12) | 1.12 (0.841.51) | 0.44 |
| HIV/AIDS | 10 (2) | 66 (2) | 1.36 (0.692.67) | 0.38 |
| Charlson composite score | ||||
| 02 | 223 (53) | 2179 (58) | Ref | |
| 35 | 117 (28) | 921 (24) | 1.24 (0.981.57) | 0.07 |
| 6 | 85(20) | 671 (18) | 1.24 (0.951.61) | 0.11 |
| Case statusc | ||||
| HCFO/HCFA | 203 (48) | 2331 (62) | Ref | |
| CA or unknown | 57 (13) | 595 (16) | 1.10 (0.811.50) | 0.54 |
| CO/HCFA, indeterminate, or non‐ BJHHCFA | 165 (39) | 845 (22) | 2.24 (1.802.79) | <0.001 |
| Prior hospitalizations | ||||
| Admitted from another healthcare facility | 109 (26) | 1018 (27) | 0.93 (0.741.17) | 0.55 |
| No. of inpatient admissions in previous 60 days | <0.001 | |||
| 0 | 200 (47) | 2310 (61) | Ref | |
| 1 | 150 (35) | 1020 (27) | 1.70 (1.362.13) | <0.001 |
| 2+ | 75 (18) | 441 (12) | 1.96 (1.482.61) | <0.001 |
| Baseline laboratory datad | ||||
| Low albumin at iCDI | 50 (12) | 548 (15) | 0.78 (0.581.07) | 0.312 |
| Low WBC at iCDI | 64 (15) | 635 (17) | 0.88 (0.661.16) | 0.36 |
| High WBC at iCDI | 247 (58) | 2027 (54) | 1.20 (0.981.46) | 0.08 |
| Low hemoglobin at iCDI | 218 (51) | 1985 (53) | 0.95 (0.781.16) | 0.61 |
| High creatinine at iCDI | 99 (23) | 862 (23) | 1.02 (0.811.30) | 0.83 |
| Low creatinine clearance at iCDI | 218 (51) | 1635 (43) | 1.38 (1.131.68) | 0.002 |
| ICU admission at iCDI | 32 (8) | 562 (15) | 0.47 (0.320.68) | <0.001 |
| Medications | ||||
| New gastric acid suppressor at iCDI | 54 (13) | 255 (7) | 2.01 (1.472.74) | <0.001 |
| Any antibiotic at iCDI | 314 (74) | 2727 (72) | 1.08 (0.861.36) | 0.49 |
| High‐risk antibiotics at iCDIe | 174 (41) | 1489 (40) | 1.06 (0.871.30) | 0.56 |
| Fluoroquinolone at iCDI | 120 (28) | 860 (23) | 1.33 (1.061.67) | 0.01 |
| Low‐risk antibiotics at iCDI | 95 (22) | 1058 (28) | 0.74 (0.580.94) | 0.01 |
| IV vancomycin at iCDI | 130 (31) | 1321 (35) | 0.82 (0.671.02) | 0.07 |
Seven factors present at the onset of iCDI were found to predict a recurrence in multivariable analysis (Table 2). Older age, CO‐HCFA status of iCDI, and 2 or more hospitalizations in the prior 60 days increased the risk of rCDI. Concomitant exposures to gastric acid suppressors, fluoroquinolone antibiotics, and high‐risk antibiotics were also significantly associated with a recurrence. Being in the intensive care unit (ICU) at the onset of iCDI was protective against rCDI in the multivariable model. This model had a C statistic of 0.642 and a Brier score of 0.089. After cross‐validation with 500 bootstrapping iterations, the model exhibited a moderately good fit (Figure 2). The prediction was particularly accurate in the lower risk ranges, with slight divergence in the risk strata over 20%. The validated model had a C statistic of 0.630 and Brier score of 0.089.
| Prediction Factors | Adjusted Odds Ratio | 95% CI |
|---|---|---|
| ||
| Agea | 1.08 | 1.021.14 |
| CO‐HCFA CDI (ref: HO‐CDI) | 1.71 | 1.322.22 |
| 2+ hospitalizations in prior 60 days (ref: 0 hospitalizations) | 1.49 | 1.082.06 |
| New gastric acid suppression at the onset of iCDI | 1.59 | 1.132.23 |
| High‐risk antibiotic at the onset of iCDIbb | 1.25 | 1.011.55 |
| Fluoroquinolone at the onset of iCDI | 1.31 | 1.041.65 |
| ICU at the onset of iCDI | 0.49 | 0.340.72 |
The sensitivity, specificity, and positive and negative predictive values of the model at various probability thresholds of rCDI are presented in Table 3. Thus, when the probability of rCDI was low, the model exhibited high sensitivity and low specificity. The situation was reversed as the probability of rCDI approached 30% (very low sensitivity and high specificity). The model's performance was optimal when the rCDI risk matched that in the current cohort, or 10.1%, with a sensitivity of 56% and specificity of 65%. However, when the rCDI risk dropped to 5%, the specificity dropped to below 30%. The sensitivity dropped to below 30% when rCDI risk rose to 15% (Table 3). Across the entire range of the probabilities tested, the negative predictive value of the model was persistently 90% or higher.
| Model Predicted Probability Cutpoint | Sensitivity | Specificity | PPV | NPV | Positive Likelihood Ratio | Negative Likelihood Ratio |
|---|---|---|---|---|---|---|
| ||||||
| 0.025 | 1.00 | 0.00 | 0.10 | 1.00 | 1.0 | Undef |
| 0.050 | 0.96 | 0.09 | 0.11 | 0.95 | 1.05 | 0.44 |
| 0.101a | 0.56 | 0.65 | 0.15 | 0.93 | 1.60 | 0.68 |
| 0.151 | 0.27 | 0.86 | 0.18 | 0.91 | 1.93 | 0.85 |
| 0.303 | 0.01 | 1.00 | 0.40 | 0.90 | Undef | 0.99 |
DISCUSSION
We have demonstrated that in a cohort of hospitalized patients with iCDI, 10% developed at least 1 episode of rCDI within 42 days of the end of iCDI treatment. The factors present at iCDI onset that predicted recurrence were age, CO‐HCFA CDI, prior hospitalization, high‐risk antibiotic and fluoroquinolone use, and gastric acid suppression. Although the model's performance was only moderate, its negative predictive value was 90% or higher across the entire range of rCDI probabilities tested. This means that the absence of this combination of risk factors in a patient with iCDI diminishes the probability of a rCDI episode to 10% or below, depending on the prior population risk for rCDI.
Prior investigators have developed prediction rules for rCDI. Hebert et al., using methodology similar to ours, constructed a model to predict the risk of rCDI among patients hospitalized with iCDI.[17] For example, the recurrence rate in their study was 23% compared to our 10%. This is likely due to the differing definitions of both iCDI and rCDI between the 2 studies. Although our definitions of hospital‐associated C difficile‐associated diarrhea (CDAD) conformed to the recommended surveillance definitions,[13] Hebert and colleagues used different definitions.[18] If this is so, the higher rate of rCDI in their study may have reflected these differences in surveillance definition, rather than the true prevalence of recurrent CDAD.
Several other studies have relied on either specialized laboratory tests alone or in combination with clinical factors. Stewart et al., in a small single‐center cohort study, reported the presence of the binary toxin to be the only independent predictor of rCDI.[19] Others have found lower antitoxin immunoglobulin levels at various times following the onset of iCDI to be predictive of a recurrence.[20, 21] A disadvantage of using these specialized tests as tools for clinical prediction is that they are not widely available in clinical practice. Even if these tests are available, their results are likely to return only after iCDI treatment has commenced. To make risk stratification more generalizable, we specifically focused on common data available in all clinical settings at the onset of iCDI.
We chose to restrict our risk stratification to factors present at the onset of iCDI for several reasons. First, earlier identification of patients at increased risk for rCDI may encourage clinicians to minimize subsequent exposures to non‐CDI antimicrobials and gastric acid suppressors. Second, newer anticlostridial therapies in development appear to target specifically CDI recurrence. The first anti‐CDI drug to be approved in 2 decades, fidaxomicin, has been shown to reduce the risk of a recurrence by nearly one‐half compared to vancomycin.[10, 11] Although in practice it is tempting to reserve this treatment for those patients who have multiple recurrences, there is no convincing evidence to date that the drug is similarly effective at reducing further recurrences in this population.[22, 23] Currently, the only population in which fidaxomicin treatment has been shown to reduce the risk of rCDI contains patients with at most 1 prior episode, whose first anti‐CDI exposure was to fidaxomicin.[10, 11] Thus, the intent of our model was to insure appropriate use of these new technologies from the perspective of both under‐ and overtreatment.
In general, most of the factors included in our model are neither novel nor surprising, including concurrent antibiotics and gastric acid suppression.[24, 25, 26, 27, 28, 29, 30] What is interesting about these exposures, however, is the fact that we measured them only at the onset of the iCDI episode. This implies that it is not merely the continuation of these medications after onset, but even exposure to them prior to the initial bout of CDI, that may promote a recurrence. This finding should give pause to the widespread practice of routinely prescribing gastric acid suppression to many hospitalized patients. It should also prompt a reexamination of antimicrobial choices for patients admitted for the treatment of infectious diseases in favor of those deemed at low risk for CDI whenever possible.
A relatively novel risk factor emerging from our model is the designation of the iCDI episode as CO‐HCFA.[30] A likely explanation for this relationship is that CO‐HCFA identifies a population of patients who are more ill, as evidenced by their prior hospitalization history. However, because recent hospitalizations themselves emerged as an independent predictor of rCDI in our model, CO‐HCFA designation clearly incorporates other factors important to this outcome.
Our data on illness severity are divergent from prior results. Previous work has found that increasing severity of illness is positively associated with the risk of a recurrence.[21, 31] In contrast, we found that the need for the ICU at the onset of iCDI appeared protective from rCDI. There are several explanations for this finding, the most likely being the competing mortality risk. Although we excluded from the study those patients who did not survive their iCDI hospitalizations, patients who received care in an ICU were more likely to die in the rCDI risk period than patients who did not receive care in an ICU (data not shown). Another potential explanation for this observation is that patients who develop iCDI while in the ICU may generally get more aggressive care than those contracting it on other wards, resulting in a lower risk for recurrence.
The recurrence rate in the current study is at the lower limit of what has been reported previously either in the meta‐analysis by Garey (13%50%) or in recent randomized controlled trials (25%).[10, 11, 25] This is likely due to our case identification pathway, and ascertainment bias is a potential limitation of our study. Patients with mild recurrent CDI diagnosed and treated as outpatients were not captured in our study unless their toxin assay was performed by the BJH laboratory (approximately 15% of specimens submitted to the BJH microbiology laboratory come from outpatients or affiliated outpatient or skilled nursing facilities). Similarly, recurrences diagnosed at other inpatient facilities were not captured in our study unless they were transferred to BJH for care. On the other hand, rCDI in randomized trials may be subject to a detection bias, because enrolled patients are prospectively monitored for and instructed to seek testing for recurrent diarrhea.
Our study also has limitations inherent to observational data such as confounding. We adjusted for all the available relevant potential confounders in the regression model. However, the possibility of residual confounding remains. Because our cohort was too small for a split‐cohort model validation, we employed a bootstrap method to cross‐validate our results. However, the model requires further validation in a prospective cohort in the future. The biggest limitation of our model, however, is its generalizability, because the data reflect patients and treatment patterns at an urban academic medical center, and may not mirror those of institutions with different characteristics or patients with iCDI diagnosed and managed completely in the outpatient setting.
In summary, we have developed a model to predict iCDI patients' risk of recurrence. The advantage of our model is the availability of all the factors at the onset of iCDI, when treatment decisions need to be made. Although far from perfect in its ability to discriminate those who will from those who will not develop a recurrence, it should serve as a beginning step in the direction of appropriately aggressive care that may result not only in diminishing the pool of this infection, but also in containing its spiraling costs. The cost‐benefit balance of these decisions needs to be examined explicitly, not only in terms of the financial cost of over‐ or undertreatment, but with respect to the implications of such overtreatment on development of resistance to newer anticlostridial agents.
Disclosures
This study was funded by Cubist Pharmaceuticals, Jersey City, New Jersey. The data in the article were presented in part as a poster presentation at IDWeek 2012, San Diego, California, October 1721, 2012. The authors report no conflicts of interest.
Clostridium difficile infection (CDI) is a serious and costly condition whose volume in US hospitals has doubled over the last decade.[1, 2, 3] Along with this rise in incidence, its severity has also increased. Although in the United States there has been a doubling in age‐adjusted case fatality, in the same time period Canadian studies reported a high and increasing CDI‐associated case fatality in the setting of an outbreak of a novel epidemic hypervirulent strain BI/NAP1/027.[2, 4, 5, 6] The costs of CDI range widely ($2500 to $13,000 per hospitalization), with cumulative annual cost to the US healthcare system estimated at nearly $5 billion.[7, 8, 9]
One of the drivers of these clinical and economic outcomes is CDI recurrence (rCDI). In 2 recent randomized controlled trials, up to 25% of patients with an initial CDI (iCDI) episode developed rCDI.[10, 11] There are few data that quantify the impact of rCDI on quality of life and survival. However, patients often are readmitted to the hospital with rCDI, and physicians who treat patients with multiple episodes of rCDI can attest to the devastating toll it takes on the lives of the patients and their families (personal communications from numerous patients to E.R.D.).[12] Reducing the incidence of rCDI may significantly improve the course of this disease.
The advent of such new treatments as fidaxomicin aimed at rCDI is promising.[10, 11] However, evidence for its efficacy so far is limited to treatment‐naive iCDI patients, thus challenging clinicians to identify patients at high risk for rCDI at iCDI onset. To address this challenge, we set out to develop a bedside prediction model for rCDI based on the factors present and routinely available at the onset of iCDI.
METHODS
Study Design and Data Source
We conducted a retrospective single‐center cohort study to examine the factors present at the onset of iCDI that impact the incidence of rCDI among hospitalized patients. Patients were included in the study if they were adults (18 years) hospitalized at Barnes‐Jewish Hospital (BJH), St. Louis, Missouri, between January 1, 2003 and December 31, 2009, and who had a positive toxin assay for C difficile in the setting of unformed stools and no history of CDI in the previous 60 days (as defined by positive toxin assay). Patients were excluded if they either died during or were discharged to hospice from the iCDI hospitalization. Cases of iCDI were categorized according to published surveillance definitions as community onset‐healthcare facility associated (CO‐HCFA), healthcare facility onset, and community associated.[13] Notably, the CO‐HCFA category included surveillance definitions for both CO‐HCFA and indeterminate cases. We defined rCDI as a repeat positive toxin within 42 days following the end of iCDI treatment. This period of risk for rCDI was chosen because the current surveillance definition for rCDI is a new episode of CDI occurring within 8 weeks from the last episode of CDI, with the assumption the patient would receive 10 to 14 days of CDI treatment at the beginning of the 8‐week period.[14] Medical charts were reviewed for all readmissions during the recurrence risk period to identify patients diagnosed with rCDI by methods other than toxin assay. A study enrollment flow chart is shown in Figure 1.
Demographic and clinical data were derived from the BJH medical informatics databases and the BJH electronic medical records (see Supporting Appendix Table 1 in the online version of this article). Comorbidities were grouped using the Charlson‐Deyo categories.[15] All variables were limited to data that are consistent throughout a hospitalization (eg, race or age) or were present within 48 hours of iCDI (eg, medications).
Model Development and Validation
First, we examined risk factors for rCDI present at the time of the iCDI diagnosis and initiation of iCDI therapy. We used principal‐component analyses, corresponding analyses, and cluster analyses to reduce the data dimensions by combining variables reflecting the same underlying construct.[16] Several antibiotic categories were created. The high‐risk category included cephalosporins, clindamycin, and aminopenicillins.[17] Other categories examined separately were fluoroquinolones, intravenous vancomycin, and antibiotics considered low risk (all other drugs not encompassed in the prior categories). Proton pump inhibitor and histamine 2 receptor‐blockers were combined into a single variable of gastric acid suppressors.
We developed a logistic regression model to identify a set of variables that best predicted the risk of rCDI. Variables with P 0.20 on univariate analyses were included in multivariable models. Backward elimination was used to determine the final model (P 0.1 for removal). The model's discrimination was examined via the C statistic and calibration through Brier score.[16] A C statistic value of 0.5 implies that the model is no better than chance, whereas the value of 1.0 means that the model is perfect in differentiating cases from noncases. A Brier score closer to zero indicates better model calibration, or how closely the predicted probabilities for rCDI match the actual observed probabilities. We validated the model using the bootstrap method with 500 iterations. To explore its properties as a decision tool to help make the decision to initiate an intervention to prevent rCDI, we tested the model's sensitivity, specificity, and positive and negative predictive values at various thresholds of prior probability of rCDI.
RESULTS
Among the 4196 patients with iCDI enrolled in the study, 425 (10.1%) developed at least 1 recurrence within 42 days of the end of iCDI treatment (Table 1). Compared to patients without a recurrence, in univariate analysis those with an rCDI episode were older and had a greater comorbidity burden. In particular, diabetes mellitus (odds ratio 1.34; 95% confidence interval [CI], 1.08‐1.66) and cerebrovascular disease (odds ratio 1.47; 95% CI, 1.04‐2.08) were significantly more prevalent in the rCDI group. The index CDI episode for patients with rCDI was approximately twice as likely to fit the surveillance definition for CO‐HCFA than the index episode for those without a recurrence (odds ratio 2.24; 95% CI, 1.80‐2.79). Commensurately, patients with rCDI also had greater odds for experiencing multiple recent hospitalizations than those without rCDI. Neither type of CDI treatment (oral metronidazole vs oral vancomycin vs both), nor duration, was significantly associated with recurrence.
| Patient Characteristics | Patients Who Developed rCDI, N = 425 | Patients Who Did Not Develop rCDI, n = 3771) | Odds Ratio (95% CI) | P Value |
|---|---|---|---|---|
| ||||
| Demographics | ||||
| Age, y, median (range)a | 64.8(18.398.2) | 61.6(18.0102.4) | 1.10 (1.041.16) | <0.001 |
| Female | 210 (49) | 1822 (48) | 1.05 (0.861.28) | 0.67 |
| Nonwhite race | 149 (35) | 1149 (31) | 1.23 (1.001.52) | 0.05 |
| Comorbiditiesb | ||||
| Myocardial infarction | 40 (9) | 328 (9) | 1.10 (0.771.54) | 0.62 |
| Congestive heart failure | 108 (25) | 854 (23) | 1.17 (0.931.47) | 0.19 |
| Peripheral vascular disease | 34 (8) | 269 (7) | 1.13 (0.781.64) | 0.51 |
| Cerebrovascular disease | 41 (10) | 256 (7) | 1.47 (1.042.08) | 0.03 |
| Chronic renal failure | 21 (5) | 190 (5) | 0.98 (0.621.56) | 0.94 |
| Dementia | 5 (1) | 23 (1) | 1.94 (0.735.14) | 0.18 |
| Chronic obstructive pulmonary disease | 116 (27) | 911 (24) | 1.18 (0.941.48) | 0.15 |
| Rheumatologic disease | 18 (4) | 146 (4) | 1.10 (0.671.81) | 0.71 |
| Peptic ulcer disease | 20 (5) | 154 (4) | 1.16 (0.721.87) | 0.54 |
| Mild liver disease | 17 (4) | 201 (5) | 0.74 (0.451.23) | 0.25 |
| Moderate‐to‐severe liver disease | 12 (3) | 134 (4) | 0.79 (0.431.44) | 0.44 |
| Diabetes, any | 135 (32) | 974 (26) | 1.34 (1.081.66) | 0.009 |
| Paraplegia or hemiplegia | 12 (3) | 77 (2) | 1.38 (0.742.55) | 0.31 |
| Any malignancy (excluding leukemia/lymphoma) | 83 (20) | 770 (20) | 0.95 (0.741.22) | 0.67 |
| Leukemia or lymphoma | 78 (18) | 660 (18) | 1.06 (0.821.38) | 0.66 |
| Metastatic solid tumor | 56 (13) | 449 (12) | 1.12 (0.841.51) | 0.44 |
| HIV/AIDS | 10 (2) | 66 (2) | 1.36 (0.692.67) | 0.38 |
| Charlson composite score | ||||
| 02 | 223 (53) | 2179 (58) | Ref | |
| 35 | 117 (28) | 921 (24) | 1.24 (0.981.57) | 0.07 |
| 6 | 85(20) | 671 (18) | 1.24 (0.951.61) | 0.11 |
| Case statusc | ||||
| HCFO/HCFA | 203 (48) | 2331 (62) | Ref | |
| CA or unknown | 57 (13) | 595 (16) | 1.10 (0.811.50) | 0.54 |
| CO/HCFA, indeterminate, or non‐ BJHHCFA | 165 (39) | 845 (22) | 2.24 (1.802.79) | <0.001 |
| Prior hospitalizations | ||||
| Admitted from another healthcare facility | 109 (26) | 1018 (27) | 0.93 (0.741.17) | 0.55 |
| No. of inpatient admissions in previous 60 days | <0.001 | |||
| 0 | 200 (47) | 2310 (61) | Ref | |
| 1 | 150 (35) | 1020 (27) | 1.70 (1.362.13) | <0.001 |
| 2+ | 75 (18) | 441 (12) | 1.96 (1.482.61) | <0.001 |
| Baseline laboratory datad | ||||
| Low albumin at iCDI | 50 (12) | 548 (15) | 0.78 (0.581.07) | 0.312 |
| Low WBC at iCDI | 64 (15) | 635 (17) | 0.88 (0.661.16) | 0.36 |
| High WBC at iCDI | 247 (58) | 2027 (54) | 1.20 (0.981.46) | 0.08 |
| Low hemoglobin at iCDI | 218 (51) | 1985 (53) | 0.95 (0.781.16) | 0.61 |
| High creatinine at iCDI | 99 (23) | 862 (23) | 1.02 (0.811.30) | 0.83 |
| Low creatinine clearance at iCDI | 218 (51) | 1635 (43) | 1.38 (1.131.68) | 0.002 |
| ICU admission at iCDI | 32 (8) | 562 (15) | 0.47 (0.320.68) | <0.001 |
| Medications | ||||
| New gastric acid suppressor at iCDI | 54 (13) | 255 (7) | 2.01 (1.472.74) | <0.001 |
| Any antibiotic at iCDI | 314 (74) | 2727 (72) | 1.08 (0.861.36) | 0.49 |
| High‐risk antibiotics at iCDIe | 174 (41) | 1489 (40) | 1.06 (0.871.30) | 0.56 |
| Fluoroquinolone at iCDI | 120 (28) | 860 (23) | 1.33 (1.061.67) | 0.01 |
| Low‐risk antibiotics at iCDI | 95 (22) | 1058 (28) | 0.74 (0.580.94) | 0.01 |
| IV vancomycin at iCDI | 130 (31) | 1321 (35) | 0.82 (0.671.02) | 0.07 |
Seven factors present at the onset of iCDI were found to predict a recurrence in multivariable analysis (Table 2). Older age, CO‐HCFA status of iCDI, and 2 or more hospitalizations in the prior 60 days increased the risk of rCDI. Concomitant exposures to gastric acid suppressors, fluoroquinolone antibiotics, and high‐risk antibiotics were also significantly associated with a recurrence. Being in the intensive care unit (ICU) at the onset of iCDI was protective against rCDI in the multivariable model. This model had a C statistic of 0.642 and a Brier score of 0.089. After cross‐validation with 500 bootstrapping iterations, the model exhibited a moderately good fit (Figure 2). The prediction was particularly accurate in the lower risk ranges, with slight divergence in the risk strata over 20%. The validated model had a C statistic of 0.630 and Brier score of 0.089.
| Prediction Factors | Adjusted Odds Ratio | 95% CI |
|---|---|---|
| ||
| Agea | 1.08 | 1.021.14 |
| CO‐HCFA CDI (ref: HO‐CDI) | 1.71 | 1.322.22 |
| 2+ hospitalizations in prior 60 days (ref: 0 hospitalizations) | 1.49 | 1.082.06 |
| New gastric acid suppression at the onset of iCDI | 1.59 | 1.132.23 |
| High‐risk antibiotic at the onset of iCDIbb | 1.25 | 1.011.55 |
| Fluoroquinolone at the onset of iCDI | 1.31 | 1.041.65 |
| ICU at the onset of iCDI | 0.49 | 0.340.72 |
The sensitivity, specificity, and positive and negative predictive values of the model at various probability thresholds of rCDI are presented in Table 3. Thus, when the probability of rCDI was low, the model exhibited high sensitivity and low specificity. The situation was reversed as the probability of rCDI approached 30% (very low sensitivity and high specificity). The model's performance was optimal when the rCDI risk matched that in the current cohort, or 10.1%, with a sensitivity of 56% and specificity of 65%. However, when the rCDI risk dropped to 5%, the specificity dropped to below 30%. The sensitivity dropped to below 30% when rCDI risk rose to 15% (Table 3). Across the entire range of the probabilities tested, the negative predictive value of the model was persistently 90% or higher.
| Model Predicted Probability Cutpoint | Sensitivity | Specificity | PPV | NPV | Positive Likelihood Ratio | Negative Likelihood Ratio |
|---|---|---|---|---|---|---|
| ||||||
| 0.025 | 1.00 | 0.00 | 0.10 | 1.00 | 1.0 | Undef |
| 0.050 | 0.96 | 0.09 | 0.11 | 0.95 | 1.05 | 0.44 |
| 0.101a | 0.56 | 0.65 | 0.15 | 0.93 | 1.60 | 0.68 |
| 0.151 | 0.27 | 0.86 | 0.18 | 0.91 | 1.93 | 0.85 |
| 0.303 | 0.01 | 1.00 | 0.40 | 0.90 | Undef | 0.99 |
DISCUSSION
We have demonstrated that in a cohort of hospitalized patients with iCDI, 10% developed at least 1 episode of rCDI within 42 days of the end of iCDI treatment. The factors present at iCDI onset that predicted recurrence were age, CO‐HCFA CDI, prior hospitalization, high‐risk antibiotic and fluoroquinolone use, and gastric acid suppression. Although the model's performance was only moderate, its negative predictive value was 90% or higher across the entire range of rCDI probabilities tested. This means that the absence of this combination of risk factors in a patient with iCDI diminishes the probability of a rCDI episode to 10% or below, depending on the prior population risk for rCDI.
Prior investigators have developed prediction rules for rCDI. Hebert et al., using methodology similar to ours, constructed a model to predict the risk of rCDI among patients hospitalized with iCDI.[17] For example, the recurrence rate in their study was 23% compared to our 10%. This is likely due to the differing definitions of both iCDI and rCDI between the 2 studies. Although our definitions of hospital‐associated C difficile‐associated diarrhea (CDAD) conformed to the recommended surveillance definitions,[13] Hebert and colleagues used different definitions.[18] If this is so, the higher rate of rCDI in their study may have reflected these differences in surveillance definition, rather than the true prevalence of recurrent CDAD.
Several other studies have relied on either specialized laboratory tests alone or in combination with clinical factors. Stewart et al., in a small single‐center cohort study, reported the presence of the binary toxin to be the only independent predictor of rCDI.[19] Others have found lower antitoxin immunoglobulin levels at various times following the onset of iCDI to be predictive of a recurrence.[20, 21] A disadvantage of using these specialized tests as tools for clinical prediction is that they are not widely available in clinical practice. Even if these tests are available, their results are likely to return only after iCDI treatment has commenced. To make risk stratification more generalizable, we specifically focused on common data available in all clinical settings at the onset of iCDI.
We chose to restrict our risk stratification to factors present at the onset of iCDI for several reasons. First, earlier identification of patients at increased risk for rCDI may encourage clinicians to minimize subsequent exposures to non‐CDI antimicrobials and gastric acid suppressors. Second, newer anticlostridial therapies in development appear to target specifically CDI recurrence. The first anti‐CDI drug to be approved in 2 decades, fidaxomicin, has been shown to reduce the risk of a recurrence by nearly one‐half compared to vancomycin.[10, 11] Although in practice it is tempting to reserve this treatment for those patients who have multiple recurrences, there is no convincing evidence to date that the drug is similarly effective at reducing further recurrences in this population.[22, 23] Currently, the only population in which fidaxomicin treatment has been shown to reduce the risk of rCDI contains patients with at most 1 prior episode, whose first anti‐CDI exposure was to fidaxomicin.[10, 11] Thus, the intent of our model was to insure appropriate use of these new technologies from the perspective of both under‐ and overtreatment.
In general, most of the factors included in our model are neither novel nor surprising, including concurrent antibiotics and gastric acid suppression.[24, 25, 26, 27, 28, 29, 30] What is interesting about these exposures, however, is the fact that we measured them only at the onset of the iCDI episode. This implies that it is not merely the continuation of these medications after onset, but even exposure to them prior to the initial bout of CDI, that may promote a recurrence. This finding should give pause to the widespread practice of routinely prescribing gastric acid suppression to many hospitalized patients. It should also prompt a reexamination of antimicrobial choices for patients admitted for the treatment of infectious diseases in favor of those deemed at low risk for CDI whenever possible.
A relatively novel risk factor emerging from our model is the designation of the iCDI episode as CO‐HCFA.[30] A likely explanation for this relationship is that CO‐HCFA identifies a population of patients who are more ill, as evidenced by their prior hospitalization history. However, because recent hospitalizations themselves emerged as an independent predictor of rCDI in our model, CO‐HCFA designation clearly incorporates other factors important to this outcome.
Our data on illness severity are divergent from prior results. Previous work has found that increasing severity of illness is positively associated with the risk of a recurrence.[21, 31] In contrast, we found that the need for the ICU at the onset of iCDI appeared protective from rCDI. There are several explanations for this finding, the most likely being the competing mortality risk. Although we excluded from the study those patients who did not survive their iCDI hospitalizations, patients who received care in an ICU were more likely to die in the rCDI risk period than patients who did not receive care in an ICU (data not shown). Another potential explanation for this observation is that patients who develop iCDI while in the ICU may generally get more aggressive care than those contracting it on other wards, resulting in a lower risk for recurrence.
The recurrence rate in the current study is at the lower limit of what has been reported previously either in the meta‐analysis by Garey (13%50%) or in recent randomized controlled trials (25%).[10, 11, 25] This is likely due to our case identification pathway, and ascertainment bias is a potential limitation of our study. Patients with mild recurrent CDI diagnosed and treated as outpatients were not captured in our study unless their toxin assay was performed by the BJH laboratory (approximately 15% of specimens submitted to the BJH microbiology laboratory come from outpatients or affiliated outpatient or skilled nursing facilities). Similarly, recurrences diagnosed at other inpatient facilities were not captured in our study unless they were transferred to BJH for care. On the other hand, rCDI in randomized trials may be subject to a detection bias, because enrolled patients are prospectively monitored for and instructed to seek testing for recurrent diarrhea.
Our study also has limitations inherent to observational data such as confounding. We adjusted for all the available relevant potential confounders in the regression model. However, the possibility of residual confounding remains. Because our cohort was too small for a split‐cohort model validation, we employed a bootstrap method to cross‐validate our results. However, the model requires further validation in a prospective cohort in the future. The biggest limitation of our model, however, is its generalizability, because the data reflect patients and treatment patterns at an urban academic medical center, and may not mirror those of institutions with different characteristics or patients with iCDI diagnosed and managed completely in the outpatient setting.
In summary, we have developed a model to predict iCDI patients' risk of recurrence. The advantage of our model is the availability of all the factors at the onset of iCDI, when treatment decisions need to be made. Although far from perfect in its ability to discriminate those who will from those who will not develop a recurrence, it should serve as a beginning step in the direction of appropriately aggressive care that may result not only in diminishing the pool of this infection, but also in containing its spiraling costs. The cost‐benefit balance of these decisions needs to be examined explicitly, not only in terms of the financial cost of over‐ or undertreatment, but with respect to the implications of such overtreatment on development of resistance to newer anticlostridial agents.
Disclosures
This study was funded by Cubist Pharmaceuticals, Jersey City, New Jersey. The data in the article were presented in part as a poster presentation at IDWeek 2012, San Diego, California, October 1721, 2012. The authors report no conflicts of interest.
- , , . Clostridium difficile infection in patients discharged from US short‐stay hospitals, 1996–2003. Emerge Infect Dis. 2006;12:409–415.
- , , . Increase in adult Clostridium difficile‐related hospitalizations and case‐fatality rate, United States, 2000–2005. Emerg Infect Dis. 2008;14:929–931.
- , , . Clostridium difficile infections (CDI) in hospital stays, 2009. HCUP statistical brief #124. Rockville, MD: Agency for Healthcare Research and Quality; 2012. Available at: http://www.hcup‐us.ahrq.gov/reports/statbriefs/sb124.pdf. Accessed July 19, 2013.
- , , , et al. A predominantly clonal multi‐institutional outbreak of Clostridium difficile‐associated diarrhea with high morbidity and mortality. N Engl J Med. 2005;353:2442–2449.
- , , , et al. Clostridium difficile‐associated diarrhea in a region of Quebec from 1991 to 2003: a changing pattern of disease severity. CMAJ. 2004;171:466–472.
- , , , et al. An epidemic, toxin gene‐variant strain of Clostridium difficile. N Engl J Med. 2005;353(23):2433–2441.
- , , , , . Short‐ and long‐term attributable costs of Clostridium difficile‐associated disease in nonsurgical inpatients. Clin Infect Dis. 2008;46(4):497–504.
- , , , . The emerging infectious challenge of Clostridium difficile‐associated disease in Massachusetts hospitals: clinical and economic consequences. Infect Control Hosp Epidemiol. 2007;28:1219–1227.
- , . Burden of Clostridium difficile on the healthcare system. Clin Infect Dis. 2012;55(suppl 2):S88–S92.
- , , , et al. Fidaxomicin versus vancomycin for infection with Clostridium difficile in Europe, Canada, and the USA: a double‐blind, non‐inferiority, randomised controlled trial. Lancet Infect Dis. 2012;12(4):281–289.
- , , , et al. Fidaxomicin versus vancomycin for Clostridium difficile infection. N Engl J Med. 2011;364:422–431.
- , , , , , . Recurrent Clostridium difficile disease: epidemiology and clinical characteristics. Infect Control Hosp Epidemiol. 1999;20:43–50.
- , , , et al. Clinical practice guidelines for Clostridium difficile infection in adults: 2010 update by the Society for Healthcare Epidemiology of America (SHEA) and the Infectious Diseases Society of America (IDSA). Infect Control Hosp Epidemiol. 2010;31:431–455.
- , , , , , ; Ad Hoc Clostridium difficile Surveillance Working Group. Recommendations for surveillance of Clostridium difficile‐associated disease. Infect Control Hosp Epidemiol. 2007;28:140–145.
- , , . Adapting a clinical comorbidity index for use with ICD‐9‐CM administrative databases. J Clin Epidemiol. 1992;45:613–619.
- , , , . Measures for evaluating model performance. Proceedings of the Biometrics Section. Alexandria, VA: American Statistical Association, Biometrics Section; 1997:253–258.
- , , , et al. Development and validation of a Clostridium difficile infection risk prediction model. Infect Control Hosp Epidemiol. 2011;32:360–366.
- , , , . Electronic health record‐based detection of risk factors for Clostridium difficile infection relapse. Infect Control Hosp Epidemiol. 2013;34:407–414.
- , , . Predicting recurrence of C. difficile colitis using bacterial virulence factors: binary toxin is the key. J Gastrointest Surg. 2013;17:118–125.
- , , , . Association between antibody response to toxin A and protection against recurrent Clostridium difficile diarrhoea. Lancet. 2001;357:189–193.
- , , , et al. Prospective derivation and validation of a clinical prediction rule for recurrent Clostridium difficile infection. Gastroenterology. 2009;136:1206–1214.
- . Fidaxomicin failures in recurrent Clostridium difficile infection: a problem of timing. Clin Infect Dis. 2012;55:613–614.
- , . Fidaxomicin “chaser” regimen following vancomycin for patients with multiple Clostridium difficile recurrences. Clin Infect Dis. 2013;56:309–310.
- , , , et al. Predictors of first recurrence of Clostridium difficile infection: implications for initial management. Clin Infect Dis. 2012;55(suppl 2):S77–S87.
- , , , . Meta‐analysis to assess risk factors for recurrent Clostridium difficile infection. J Hosp Infect. 2008;70:298–304.
- , , , , , . Recurrent Clostridium difficile diarrhea: characteristics of and risk factors for patients enrolled in a prospective, randomized, double‐blinded trial. Clin Infect Dis. 1997;24:324–333.
- , , , , . Association of proton‐pump inhibitors with outcomes in Clostridium difficile colitis. Am J Health Syst Pharm. 2007;64:2359–2363.
- , , , et al. Proton pump inhibitors as a risk factor for recurrence of Clostridium‐difficile‐associated diarrhea. World J Gastroenterol. 2010;16:3573–3577.
- , , . Proton pump inhibitor use and recurrent Clostridium difficile‐associated disease: a case‐control analysis matched by propensity score. J Clin Gastroenterol. 2012;46:397–400.
- , , , , , . Risk of Clostridium difficile infection with acid suppressing drugs and antibiotics: meta‐analysis. Am J Gastroenterol. 2012;107(7):1011–1019.
- , , , et al. Risk factors for early recurrent Clostridium difficile‐associated diarrhea. Clin Infect Dis. 1998;26:954–959.
- , , . Clostridium difficile infection in patients discharged from US short‐stay hospitals, 1996–2003. Emerge Infect Dis. 2006;12:409–415.
- , , . Increase in adult Clostridium difficile‐related hospitalizations and case‐fatality rate, United States, 2000–2005. Emerg Infect Dis. 2008;14:929–931.
- , , . Clostridium difficile infections (CDI) in hospital stays, 2009. HCUP statistical brief #124. Rockville, MD: Agency for Healthcare Research and Quality; 2012. Available at: http://www.hcup‐us.ahrq.gov/reports/statbriefs/sb124.pdf. Accessed July 19, 2013.
- , , , et al. A predominantly clonal multi‐institutional outbreak of Clostridium difficile‐associated diarrhea with high morbidity and mortality. N Engl J Med. 2005;353:2442–2449.
- , , , et al. Clostridium difficile‐associated diarrhea in a region of Quebec from 1991 to 2003: a changing pattern of disease severity. CMAJ. 2004;171:466–472.
- , , , et al. An epidemic, toxin gene‐variant strain of Clostridium difficile. N Engl J Med. 2005;353(23):2433–2441.
- , , , , . Short‐ and long‐term attributable costs of Clostridium difficile‐associated disease in nonsurgical inpatients. Clin Infect Dis. 2008;46(4):497–504.
- , , , . The emerging infectious challenge of Clostridium difficile‐associated disease in Massachusetts hospitals: clinical and economic consequences. Infect Control Hosp Epidemiol. 2007;28:1219–1227.
- , . Burden of Clostridium difficile on the healthcare system. Clin Infect Dis. 2012;55(suppl 2):S88–S92.
- , , , et al. Fidaxomicin versus vancomycin for infection with Clostridium difficile in Europe, Canada, and the USA: a double‐blind, non‐inferiority, randomised controlled trial. Lancet Infect Dis. 2012;12(4):281–289.
- , , , et al. Fidaxomicin versus vancomycin for Clostridium difficile infection. N Engl J Med. 2011;364:422–431.
- , , , , , . Recurrent Clostridium difficile disease: epidemiology and clinical characteristics. Infect Control Hosp Epidemiol. 1999;20:43–50.
- , , , et al. Clinical practice guidelines for Clostridium difficile infection in adults: 2010 update by the Society for Healthcare Epidemiology of America (SHEA) and the Infectious Diseases Society of America (IDSA). Infect Control Hosp Epidemiol. 2010;31:431–455.
- , , , , , ; Ad Hoc Clostridium difficile Surveillance Working Group. Recommendations for surveillance of Clostridium difficile‐associated disease. Infect Control Hosp Epidemiol. 2007;28:140–145.
- , , . Adapting a clinical comorbidity index for use with ICD‐9‐CM administrative databases. J Clin Epidemiol. 1992;45:613–619.
- , , , . Measures for evaluating model performance. Proceedings of the Biometrics Section. Alexandria, VA: American Statistical Association, Biometrics Section; 1997:253–258.
- , , , et al. Development and validation of a Clostridium difficile infection risk prediction model. Infect Control Hosp Epidemiol. 2011;32:360–366.
- , , , . Electronic health record‐based detection of risk factors for Clostridium difficile infection relapse. Infect Control Hosp Epidemiol. 2013;34:407–414.
- , , . Predicting recurrence of C. difficile colitis using bacterial virulence factors: binary toxin is the key. J Gastrointest Surg. 2013;17:118–125.
- , , , . Association between antibody response to toxin A and protection against recurrent Clostridium difficile diarrhoea. Lancet. 2001;357:189–193.
- , , , et al. Prospective derivation and validation of a clinical prediction rule for recurrent Clostridium difficile infection. Gastroenterology. 2009;136:1206–1214.
- . Fidaxomicin failures in recurrent Clostridium difficile infection: a problem of timing. Clin Infect Dis. 2012;55:613–614.
- , . Fidaxomicin “chaser” regimen following vancomycin for patients with multiple Clostridium difficile recurrences. Clin Infect Dis. 2013;56:309–310.
- , , , et al. Predictors of first recurrence of Clostridium difficile infection: implications for initial management. Clin Infect Dis. 2012;55(suppl 2):S77–S87.
- , , , . Meta‐analysis to assess risk factors for recurrent Clostridium difficile infection. J Hosp Infect. 2008;70:298–304.
- , , , , , . Recurrent Clostridium difficile diarrhea: characteristics of and risk factors for patients enrolled in a prospective, randomized, double‐blinded trial. Clin Infect Dis. 1997;24:324–333.
- , , , , . Association of proton‐pump inhibitors with outcomes in Clostridium difficile colitis. Am J Health Syst Pharm. 2007;64:2359–2363.
- , , , et al. Proton pump inhibitors as a risk factor for recurrence of Clostridium‐difficile‐associated diarrhea. World J Gastroenterol. 2010;16:3573–3577.
- , , . Proton pump inhibitor use and recurrent Clostridium difficile‐associated disease: a case‐control analysis matched by propensity score. J Clin Gastroenterol. 2012;46:397–400.
- , , , , , . Risk of Clostridium difficile infection with acid suppressing drugs and antibiotics: meta‐analysis. Am J Gastroenterol. 2012;107(7):1011–1019.
- , , , et al. Risk factors for early recurrent Clostridium difficile‐associated diarrhea. Clin Infect Dis. 1998;26:954–959.
© 2014 Society of Hospital Medicine
Drug Resistance in Pneumonia and BSI
Administration of initially appropriate antimicrobial therapy represents a key determinant of outcome in patients with severe infection.[1, 2, 3, 4, 5, 6, 7, 8, 9] The variable patterns of antimicrobial resistance seen between and within healthcare institutions complicate the process of antibiotic selection. Although much attention has historically focused on Staphylococcus aureus, resistance among Gram‐negative pathogens has emerged as a major challenge in the care of hospitalized, and particularly critically ill, patients.[2, 10, 11] Multidrug, and more specifically carbapenem resistance, among such common organisms as Pseudomonas aeruginosa (PA) and Enterobacteriaceae represents a major treatment challenge.[2] A recent US‐based surveillance study reported that a quarter of device‐related infections in hospitalized patients were caused by carbapenem‐resistant PA.[10]
In addition to changes in resistance patterns seen among PA isolates, increasing rates of nonsusceptibility have been described among Enterobacteriaceae. Resistance rates to third‐generation cephalosporins in these pathogens have risen steadily since 1988, reaching 20% among Klebsiella pneumoniae and 5% among Escherichia coli isolates by 2004.[11] In response to this, clinicians have increasingly utilized carbapenems to treat patients with serious Gram‐negative infections. However, the development of several types of carbapenemases by Enterobacteriaceae has led to a greater prevalence of carbapenem‐resistant Enterobacteriaceae species (CRE).[12, 13, 14, 15, 16, 17, 18] In fact, a recent report from the Centers for Disease Control and Prevention (CDC) documents a rapid rise in both the prevalence and extent of CRE in the United States.[19]
These Gram‐negative multidrug‐resistant (MDR) organisms frequently cause serious infections including pneumonia and bloodstream infections (BSI). The fact that these conditions, if not addressed in a timely and appropriate manner, lead to high morbidity, mortality, and costs, makes understanding the patterns of resistance that much more critical. To gain a better understanding of the prevalence and characteristics of MDR rates among PA and carbapenem resistance in Enterobacteriaceae in patients hospitalized in the United States with pneumonia and BSI, we conducted a multicenter survey of microbiology data.
METHODS
To determine the prevalence of predefined resistance patterns among PA and Enterobacteriaceae in pneumonia and BSI specimens, we examined The Surveillance Network (TSN) database from Eurofins between years 2000 and 2009. The database has been used extensively for surveillance purposes since 1994 and has previously been described in detail.[17, 20, 21, 22, 23] Briefly, TSN is a warehouse of routine clinical microbiology data collected from a nationally representative sample of microbiology laboratories in 217 hospitals in the United States. To minimize selection bias, laboratories are included based on their geography and the demographics of the populations they serve.[20] Only clinically significant samples are reported. No personal identifying information for source patients is available in this database. Only source laboratories that perform antimicrobial susceptibility testing according standard US Food and Drug Administration‐approved testing methods and interpret susceptibility in accordance with the Clinical Laboratory Standards Institute breakpoints are included.[24] All enrolled laboratories undergo a pre‐enrollment site visit. Logical filters are used for routine quality control to detect unusual susceptibility profiles and to ensure appropriate testing methods. Repeat testing and reporting are done as necessary.[20]
Laboratory samples are reported as susceptible, intermediate, or resistant.[24] We required that samples have susceptibility data for each of the antimicrobials needed to determine their resistance phenotype. These susceptibility patterns served as phenotypic surrogates for resistance. We grouped intermediate samples together with the resistant ones for the purposes of the current analysis. Duplicate isolates were excluded. Only samples representing 1 of the 2 infections of interest, pneumonia and BSI, were included.
We defined MDR‐PA as any isolate resistant to 3 of the following drug classes: aminoglycoside (gentamicin), antipseudomonal penicillin (piperacillin‐tazobactam), antipseudomonal cephalosporin (ceftazidime), carbapenems (imipenem, meropenem), and fluoroquinolone (ciprofloxacin). Enterobacteriaceae were considered CRE if resistant to both a third‐generation cephalosporin and a carbapenem. We examined the data by infection type, year, the 9 US Census geographical divisions, and intensive care unit (ICU) origin.
We did not pursue hypothesis testing due to a high risk of type I error in this large dataset. Therefore, only clinically important trends are highlighted.
RESULTS
Source specimen characteristics for the 205,526 PA (187,343 pneumonia and 18,183 BSI) and 95,566 Enterobacteriaceae specimens (58,810 pneumonia and 36,756 BSI) identified are presented in Table 1. The median age of the patients from which the isolates derive was similar among the PA pneumonia, Enterobacteriaceae pneumonia, and Enterobacteriaceae BSI groups, but higher in the PA BSI group. Similarly, there were differences in the gender distribution of source patients between the organisms and infections. Namely, although females represented a stable 42% of each of the infections with PA, the proportions of females with Enterobacteriaceae pneumonia (36.2%) differed from that in the BSI group (48.6%). Pneumonia specimens (34.0% PA and 39.0% Enterobacteriaceae) were more likely to originate in the ICU than those from BSI (28.4% PA and 21.1% Enterobacteriaceae).
| Pseudomonas aeruginosa, N=205,526 | Enterobacteriaceae, N=95,566 | |
|---|---|---|
| ||
| Pneumonia, n | 187,343 | 58,810 |
| Age, y, median (IQR 25, 75) | 54 (23, 71) | 55 (21, 71) |
| Gender, female, n (%) | 78,418 (41.9) | 21,305 (36.2) |
| ICU origin, n (%) | 63,755 (34.0) | 22,942 (39.0) |
| Meeting definitions of resistance, n (%) | 41,180 (22.0) | 930 (1.6) |
| BSI, n | 18,183 | 36,756 |
| Age, y, median (IQR 25, 75) | 59 (31, 75) | 55 (24, 71) |
| Gender, female, n (%) | 7,448 (41.8) | 17,871 (48.6) |
| ICU origin, n (%) | 5,170 (28.4) | 7,751 (21.1) |
| Meeting definitions of resistance, n (%) | 2,668 (14.7) | 394 (1.1) |
The prevalence of resistance among PA isolates was approximately 15‐fold higher than among Enterobacteriaceae specimens in both infection types (Table 1). This pattern persisted when stratified by infection type (pneumonia: 22.0% MDR‐PA vs 1.6% CRE; BSI: 14.7% MDR‐PA vs 1.1% CRE).
Over the time frame of the study, we detected variable patterns of resistance in the 2 groups of organisms (Figure 1). Namely, among PA in both pneumonia and BSI there was an initial rise in the proportion of MDR specimens between 2000 and 2003, followed by a stabilization until 2005, an additional rise in 2006, and a gradual decline and stabilization through 2009. These fluctuations notwithstanding, there was a net rise in MDR‐PA as a proportion of all PA from 10.7% in 2000 to 13.5% in 2009 among BSI, and from 19.2% in 2000 to 21.7% in 2009 among pneumonia specimens. Among Enterobacteriaceae, the CRE phenotype emerged in 2002 in both infection types and peaked in 2008 at 3.6% in BSI and 5.3% in pneumonia. This peak was followed by a stabilization in 2009 in BSI (3.5%) and a further decline, albeit minor, to 4.6% in pneumonia.
We noted geographic differences in the distribution of resistance (Table 2). Although MDR‐PA was more likely to originate from the East and West North Central divisions, and least likely from the New England and Mountain states, most CRE was detected in the specimens from the latter 2 regions. When stratified by ICU as the location of specimen origin, there were differences in the prevalence of resistant organisms of both types, but these differences were observed only in BSI specimens and not in pneumonia (Figure 2). That is, in BSI, the likelihood of a resistant organism originating from the ICU was approximately double that from a non‐ICU location for both MDR‐PA (21.9% vs 11.8%) and CRE (2.0% vs 0.8%).
| Census Division | MDR‐PA | CRE | ||
|---|---|---|---|---|
| BSI | Pneumonia | BSI | Pneumonia | |
| ||||
| East North Central | 20.8% | 26.9% | 2.0% | 1.9% |
| West North Central | 18.0% | 22.1% | 0.8% | 0.7% |
| East South Central | 15.8% | 20.5% | 0.1% | 0.1% |
| West South Central | 13.5% | 21.7% | 0.3% | 0.5% |
| Pacific | 13.1% | 20.3% | 0.3% | 0.3% |
| Mid‐Atlantic | 12.6% | 20.5% | 2.5% | 3.8% |
| South Atlantic | 12.6% | 21.6% | 0.9% | 1.5% |
| New England | 10.7% | 19.7% | 1.3% | 2.9% |
| Mountain | 8.5% | 19.4% | 0.4% | 1.1% |
DISCUSSION
We have demonstrated that among both pneumonia and BSI specimens, PA and Enterobacteriaceae have a high prevalence of multidrug resistance. When examined cross‐sectionally, in both pneumonia and BSI, the prevalence of MDR‐PA was approximately 15‐fold higher than the prevalence of CRE among Enterobacteriaceae. Over the time frame of the study, MDR‐PA rose and then fell and stabilized to levels only slightly higher than those observed at the beginning of the observation period. In contrast, CRE emerged and rose precipitously between 2006 and 2008, and appeared to stabilize in 2009 in both infection types. Interestingly, we observed geographic variability among resistant isolates. Specifically, the prevalence of CRE was highest in the region with a relatively low prevalence of MDR‐PA. Despite this heterogeneity geographically, resistance for both isolate types in BSI but not in pneumonia was substantially higher in the ICU than outside the ICU.
Our data enhance the current understanding of distribution of Gram‐negative resistance in the United States. A recent study by Braykov and colleagues examined time trends in the development of CRE phenotype among Klebsiella pneumoniae in the United States.[17] By focusing on this single pathogen in various infections within Eurofin's TSN database between 1999 and 2010, they pinpointed its initial emergence to year 2002, with a notably steep rise between 2006 and 2009, with some reduction in the pace of growth in 2010. We have documented an analogous rise in the CRE phenotype among all Enterobacteriaceae, particularly in pneumonia and BSI within a similar time period. Thus, our data on the 1 hand broaden the concern about this pathogen beyond just a single organism within Enterobacteraceae and a single antimicrobial class, and on the other hand serve to focus attention on 2 clinically burdensome infection types, pneumonia and BSI.
Another recent investigation reported a rise in carbapenem‐resistant Enterobacteriaceae in US hospitals over the past decade.[19] Drawing on data from multiple sources, including the dataset used for the current analysis, this study examined the patterns of single‐class resistance to carbapenems among central line‐associated BSI (CLABSI) and catheter‐associated urinary tract infection specimens. Consistent with our findings, these authors noted that the highest percentage of hospitals reporting such single‐class carbapenem‐resistant specimens were located in the Northeastern United States. They also described that the proportion of Enterobacteriaceae with single‐class carbapenem resistance rose from 0% in 2001 to 1.4% in 2010. An additional CDC analysis reported that single‐class carbapenem resistance now exists in 4.2% of Enterobacteraciae as compared to 1.2% of isolates in 2001. We confirm that this rise in single‐class resistance is echoed by a rise in the prevalence of the CRE phenotype, and provides further granularity to this problem, specifically in the setting of pneumonia and BSI.
Although CRE has become an important concern in the treatment of patients with pneumonia and BSI, MDR‐PA remains a far larger challenge in these infections. CREs appear to occur more frequently than in the past but remain relatively dwarfed by the prevalence of MDR‐PA. Our data are generally in agreement with the 2009 to 2010 data from the National Healthcare Safety Network (NHSN) maintained by the CDC, which focuses on CLABSI and ventilator‐associated pneumonia (VAP) rather than general BSI and pneumonia in US hospitals.[25] In this report, the proportion of PA that were classified as MDR according to a definition similar to ours was 15.4% in CLABSI and 17.7% in VAP. In contrast, we document that 13.5% of PA causing BSI and 21.7% causing pneumonia were due to MDR‐PA organisms. This mild divergence likely reflects the slightly different antimicrobials utilized to define MDR‐PA in the 2 studies, as well as variance in the populations examined. An additional data point reported in the NHSN study is the proportion of MDR‐PA CLABSI originating in the ICU (16.8%) versus non‐ICU hospital locations (13.3%). Although the difference we found in the prevalence of BSI by the location in the hospital was greater, we confirm that ICU specimens carry a higher risk of harboring MDR‐PA.
Our study has a number of strengths and limitations. Because we used a nationally representative database to derive our estimates, our results are highly generalizable.
The TSN database consists of microbiology samples from hospital laboratories. Although we attempted to reduce the risk of duplication, because of how samples are numbered in the database, repeat sampling remains a possibility. The definitions of resistance were based on phenotypic patterns of resistance to various antimicrobial classes. This makes our resistant organisms subject to misclassification.
In summary, although carbapenem resistance among Enterobacteriaceae has emerged as an important phenomenon, multidrug resistance among PA remains relatively more prevalent in the United States. Furthermore, over the decade examined, MDR‐PA has remained an important pathogen in pneumonia and BSI that persists across all geographic regions of the United States. Although CRE is rightfully receiving a disproportionate share of attention from public health officials, it would be shortsighted to ignore the importance of MDR‐PA as a target, not only for transmission prevention and antimicrobial stewardship, but also for new therapeutic development. Because the patterns of resistance are rapidly evolving, it is incumbent upon our public health enterprise to perform more granular real‐time surveillance to allow changes in epidemiology to inform policy and treatment decisions.
ACKNOWLEDGEMENTS
Disclosures: This study was supported by a grant from Cubist Pharmaceuticals. The authors report no conflicts of interest.
- National Nosocomial Infections Surveillance (NNIS) System Report. Am J Infect Control. 2004;32:470.
- , , , . National surveillance of antimicrobial resistance in Pseudomonas aeruginosa isolates obtained from intensive care unit patients from 1993 to 2002. Antimicrob Agents Chemother. 2004;48:4606–4610.
- , , , et al. Health care‐associated pneumonia and community‐acquired pneumonia: a single‐center experience. Antimicrob Agents Chemother. 2007;51:3568–3573.
- , , , et al. Clinical importance of delays in the initiation of appropriate antibiotic treatment for ventilator‐associated pneumonia. Chest. 2002;122:262–268.
- . Modification of empiric antibiotic treatment in patients with pneumonia acquired in the intensive care unit. ICU‐Acquired Pneumonia Study Group. Intensive Care Med. 1996;22:387–394.
- , , , , . Antimicrobial therapy escalation and hospital mortality among patients with HCAP: a single center experience. Chest. 2008:134:963–968.
- , , , et al. Surviving Sepsis Campaign: international guidelines for management of severe sepsis and septic shock: 2008. Crit Care Med. 2008;36:296–327.
- , , , , , . Inappropriate antibiotic therapy in Gram‐negative sepsis increases hospital length of stay. Crit Care Med. 2011;39:46–51.
- , , , . Inadequate antimicrobial treatment of infections: a risk factor for hospital mortality among critically ill patients. Chest. 1999;115:462–474.
- , , , et al. Antimicrobial‐resistant pathogens associated with healthcare‐associated infections: annual summary of data reported to the National Healthcare Safety Network at the Centers for Disease Control and Prevention, 2006–2007. Infect Control Hospital Epidemiol. 2008;29:996–1011.
- , ; National Nosocomial Infections Surveillance (NNIS) System. Overview of nosocomial infections caused by Gram‐negative bacilli. Clin Infect Dis. 2005;41:848–854.
- , , . The real threat of Klebsiella pneumoniae carbapenemase‐producing bacteria. Lancet Infect Dis. 2009;9:228–236.
- , , , . Household transmission of carbapenemase‐producing Klebsiella pneumoniae. Emerg Infect Dis. 2008;14:859–860.
- , , , . Isolation of imipenem‐resistant Enterobacter species: emergence of KPC‐2 carbapenemase, molecular characterization, epidemiology, and outcomes. Antimicrob Agents Chemother. 2008;52:1413–1418.
- , , , , . Outcomes of carbapenem‐resistant Klebsiella pneumoniae infection and the impact of antimicrobial and adjunctive therapies. Infect Control Hosp Epidemiol. 2008;29:1099–1106.
- , , , , , ; for the Centers for Disease Control and Prevention Epicenter Program. Emergence and rapid regional spread of Klebsiella pneumoniae carbapenemase‐producing Enterobacteriaceae. Clin Infect Dis. 2011;53:532–540.
- , , , , . Trends in resistance to carbapenems and third‐generation cephalosporins among clinical isolates of Klebsiella pneumoniae in the United States, 1999–2010. Infect Control Hosp Epidemiol. 2013;34:259–268.
- , , , . Population‐based incidence of carbapenem‐resistant Klebsiella pneumoniae along the continuum of care, Los Angeles County. Infect Control Hosp Epidemiol. 2013;34:144–150.
- Centers for Disease Control and Prevention (CDC). Vital signs: carbapenem‐resistant enterobacteriaceae. MMWR Morb Mortal Wkly Rep. 2013;62:165–170.
- , , . Antimicrobial resistance in key bloodstream bacterial isolates: electronic surveillance with the Surveillance Network Database–USA. Clin Infect Dis. 1999;29:259–263.
- , , . Community‐associated methicillin‐resistant Staphylococcus aureus in outpatients, United States, 1999–2006. Emerg Infect Dis. 2009;15:1925–1930.
- , , . Increasing resistance of Acinetobacter species to imipenem in United States hospitals, 1999–2006. Infect Control Hosp Epidemiol. 2010;31:196–197.
- , , , , . Prevalence of antimicrobial resistance in bacteria isolated from central nervous system specimens as reported by U.S. hospital laboratories from 2000 to 2002. Ann Clin Microbiol Antimicrob. 2004;3:3.
- Clinical Laboratory Standards Institute. Available at: http://www.clsi.org. Accessed July 8, 2013.
- , , , et al. Antimicrobial‐resistant pathogens associates with healthcare‐associated infections: Summary of data reported to the National Healthcare Safety Network at the Centers for Disease Control and Prevention, 2009–2010. Infect Control Hosp Epidemiol. 2013;34:1–14.
Administration of initially appropriate antimicrobial therapy represents a key determinant of outcome in patients with severe infection.[1, 2, 3, 4, 5, 6, 7, 8, 9] The variable patterns of antimicrobial resistance seen between and within healthcare institutions complicate the process of antibiotic selection. Although much attention has historically focused on Staphylococcus aureus, resistance among Gram‐negative pathogens has emerged as a major challenge in the care of hospitalized, and particularly critically ill, patients.[2, 10, 11] Multidrug, and more specifically carbapenem resistance, among such common organisms as Pseudomonas aeruginosa (PA) and Enterobacteriaceae represents a major treatment challenge.[2] A recent US‐based surveillance study reported that a quarter of device‐related infections in hospitalized patients were caused by carbapenem‐resistant PA.[10]
In addition to changes in resistance patterns seen among PA isolates, increasing rates of nonsusceptibility have been described among Enterobacteriaceae. Resistance rates to third‐generation cephalosporins in these pathogens have risen steadily since 1988, reaching 20% among Klebsiella pneumoniae and 5% among Escherichia coli isolates by 2004.[11] In response to this, clinicians have increasingly utilized carbapenems to treat patients with serious Gram‐negative infections. However, the development of several types of carbapenemases by Enterobacteriaceae has led to a greater prevalence of carbapenem‐resistant Enterobacteriaceae species (CRE).[12, 13, 14, 15, 16, 17, 18] In fact, a recent report from the Centers for Disease Control and Prevention (CDC) documents a rapid rise in both the prevalence and extent of CRE in the United States.[19]
These Gram‐negative multidrug‐resistant (MDR) organisms frequently cause serious infections including pneumonia and bloodstream infections (BSI). The fact that these conditions, if not addressed in a timely and appropriate manner, lead to high morbidity, mortality, and costs, makes understanding the patterns of resistance that much more critical. To gain a better understanding of the prevalence and characteristics of MDR rates among PA and carbapenem resistance in Enterobacteriaceae in patients hospitalized in the United States with pneumonia and BSI, we conducted a multicenter survey of microbiology data.
METHODS
To determine the prevalence of predefined resistance patterns among PA and Enterobacteriaceae in pneumonia and BSI specimens, we examined The Surveillance Network (TSN) database from Eurofins between years 2000 and 2009. The database has been used extensively for surveillance purposes since 1994 and has previously been described in detail.[17, 20, 21, 22, 23] Briefly, TSN is a warehouse of routine clinical microbiology data collected from a nationally representative sample of microbiology laboratories in 217 hospitals in the United States. To minimize selection bias, laboratories are included based on their geography and the demographics of the populations they serve.[20] Only clinically significant samples are reported. No personal identifying information for source patients is available in this database. Only source laboratories that perform antimicrobial susceptibility testing according standard US Food and Drug Administration‐approved testing methods and interpret susceptibility in accordance with the Clinical Laboratory Standards Institute breakpoints are included.[24] All enrolled laboratories undergo a pre‐enrollment site visit. Logical filters are used for routine quality control to detect unusual susceptibility profiles and to ensure appropriate testing methods. Repeat testing and reporting are done as necessary.[20]
Laboratory samples are reported as susceptible, intermediate, or resistant.[24] We required that samples have susceptibility data for each of the antimicrobials needed to determine their resistance phenotype. These susceptibility patterns served as phenotypic surrogates for resistance. We grouped intermediate samples together with the resistant ones for the purposes of the current analysis. Duplicate isolates were excluded. Only samples representing 1 of the 2 infections of interest, pneumonia and BSI, were included.
We defined MDR‐PA as any isolate resistant to 3 of the following drug classes: aminoglycoside (gentamicin), antipseudomonal penicillin (piperacillin‐tazobactam), antipseudomonal cephalosporin (ceftazidime), carbapenems (imipenem, meropenem), and fluoroquinolone (ciprofloxacin). Enterobacteriaceae were considered CRE if resistant to both a third‐generation cephalosporin and a carbapenem. We examined the data by infection type, year, the 9 US Census geographical divisions, and intensive care unit (ICU) origin.
We did not pursue hypothesis testing due to a high risk of type I error in this large dataset. Therefore, only clinically important trends are highlighted.
RESULTS
Source specimen characteristics for the 205,526 PA (187,343 pneumonia and 18,183 BSI) and 95,566 Enterobacteriaceae specimens (58,810 pneumonia and 36,756 BSI) identified are presented in Table 1. The median age of the patients from which the isolates derive was similar among the PA pneumonia, Enterobacteriaceae pneumonia, and Enterobacteriaceae BSI groups, but higher in the PA BSI group. Similarly, there were differences in the gender distribution of source patients between the organisms and infections. Namely, although females represented a stable 42% of each of the infections with PA, the proportions of females with Enterobacteriaceae pneumonia (36.2%) differed from that in the BSI group (48.6%). Pneumonia specimens (34.0% PA and 39.0% Enterobacteriaceae) were more likely to originate in the ICU than those from BSI (28.4% PA and 21.1% Enterobacteriaceae).
| Pseudomonas aeruginosa, N=205,526 | Enterobacteriaceae, N=95,566 | |
|---|---|---|
| ||
| Pneumonia, n | 187,343 | 58,810 |
| Age, y, median (IQR 25, 75) | 54 (23, 71) | 55 (21, 71) |
| Gender, female, n (%) | 78,418 (41.9) | 21,305 (36.2) |
| ICU origin, n (%) | 63,755 (34.0) | 22,942 (39.0) |
| Meeting definitions of resistance, n (%) | 41,180 (22.0) | 930 (1.6) |
| BSI, n | 18,183 | 36,756 |
| Age, y, median (IQR 25, 75) | 59 (31, 75) | 55 (24, 71) |
| Gender, female, n (%) | 7,448 (41.8) | 17,871 (48.6) |
| ICU origin, n (%) | 5,170 (28.4) | 7,751 (21.1) |
| Meeting definitions of resistance, n (%) | 2,668 (14.7) | 394 (1.1) |
The prevalence of resistance among PA isolates was approximately 15‐fold higher than among Enterobacteriaceae specimens in both infection types (Table 1). This pattern persisted when stratified by infection type (pneumonia: 22.0% MDR‐PA vs 1.6% CRE; BSI: 14.7% MDR‐PA vs 1.1% CRE).
Over the time frame of the study, we detected variable patterns of resistance in the 2 groups of organisms (Figure 1). Namely, among PA in both pneumonia and BSI there was an initial rise in the proportion of MDR specimens between 2000 and 2003, followed by a stabilization until 2005, an additional rise in 2006, and a gradual decline and stabilization through 2009. These fluctuations notwithstanding, there was a net rise in MDR‐PA as a proportion of all PA from 10.7% in 2000 to 13.5% in 2009 among BSI, and from 19.2% in 2000 to 21.7% in 2009 among pneumonia specimens. Among Enterobacteriaceae, the CRE phenotype emerged in 2002 in both infection types and peaked in 2008 at 3.6% in BSI and 5.3% in pneumonia. This peak was followed by a stabilization in 2009 in BSI (3.5%) and a further decline, albeit minor, to 4.6% in pneumonia.
We noted geographic differences in the distribution of resistance (Table 2). Although MDR‐PA was more likely to originate from the East and West North Central divisions, and least likely from the New England and Mountain states, most CRE was detected in the specimens from the latter 2 regions. When stratified by ICU as the location of specimen origin, there were differences in the prevalence of resistant organisms of both types, but these differences were observed only in BSI specimens and not in pneumonia (Figure 2). That is, in BSI, the likelihood of a resistant organism originating from the ICU was approximately double that from a non‐ICU location for both MDR‐PA (21.9% vs 11.8%) and CRE (2.0% vs 0.8%).
| Census Division | MDR‐PA | CRE | ||
|---|---|---|---|---|
| BSI | Pneumonia | BSI | Pneumonia | |
| ||||
| East North Central | 20.8% | 26.9% | 2.0% | 1.9% |
| West North Central | 18.0% | 22.1% | 0.8% | 0.7% |
| East South Central | 15.8% | 20.5% | 0.1% | 0.1% |
| West South Central | 13.5% | 21.7% | 0.3% | 0.5% |
| Pacific | 13.1% | 20.3% | 0.3% | 0.3% |
| Mid‐Atlantic | 12.6% | 20.5% | 2.5% | 3.8% |
| South Atlantic | 12.6% | 21.6% | 0.9% | 1.5% |
| New England | 10.7% | 19.7% | 1.3% | 2.9% |
| Mountain | 8.5% | 19.4% | 0.4% | 1.1% |
DISCUSSION
We have demonstrated that among both pneumonia and BSI specimens, PA and Enterobacteriaceae have a high prevalence of multidrug resistance. When examined cross‐sectionally, in both pneumonia and BSI, the prevalence of MDR‐PA was approximately 15‐fold higher than the prevalence of CRE among Enterobacteriaceae. Over the time frame of the study, MDR‐PA rose and then fell and stabilized to levels only slightly higher than those observed at the beginning of the observation period. In contrast, CRE emerged and rose precipitously between 2006 and 2008, and appeared to stabilize in 2009 in both infection types. Interestingly, we observed geographic variability among resistant isolates. Specifically, the prevalence of CRE was highest in the region with a relatively low prevalence of MDR‐PA. Despite this heterogeneity geographically, resistance for both isolate types in BSI but not in pneumonia was substantially higher in the ICU than outside the ICU.
Our data enhance the current understanding of distribution of Gram‐negative resistance in the United States. A recent study by Braykov and colleagues examined time trends in the development of CRE phenotype among Klebsiella pneumoniae in the United States.[17] By focusing on this single pathogen in various infections within Eurofin's TSN database between 1999 and 2010, they pinpointed its initial emergence to year 2002, with a notably steep rise between 2006 and 2009, with some reduction in the pace of growth in 2010. We have documented an analogous rise in the CRE phenotype among all Enterobacteriaceae, particularly in pneumonia and BSI within a similar time period. Thus, our data on the 1 hand broaden the concern about this pathogen beyond just a single organism within Enterobacteraceae and a single antimicrobial class, and on the other hand serve to focus attention on 2 clinically burdensome infection types, pneumonia and BSI.
Another recent investigation reported a rise in carbapenem‐resistant Enterobacteriaceae in US hospitals over the past decade.[19] Drawing on data from multiple sources, including the dataset used for the current analysis, this study examined the patterns of single‐class resistance to carbapenems among central line‐associated BSI (CLABSI) and catheter‐associated urinary tract infection specimens. Consistent with our findings, these authors noted that the highest percentage of hospitals reporting such single‐class carbapenem‐resistant specimens were located in the Northeastern United States. They also described that the proportion of Enterobacteriaceae with single‐class carbapenem resistance rose from 0% in 2001 to 1.4% in 2010. An additional CDC analysis reported that single‐class carbapenem resistance now exists in 4.2% of Enterobacteraciae as compared to 1.2% of isolates in 2001. We confirm that this rise in single‐class resistance is echoed by a rise in the prevalence of the CRE phenotype, and provides further granularity to this problem, specifically in the setting of pneumonia and BSI.
Although CRE has become an important concern in the treatment of patients with pneumonia and BSI, MDR‐PA remains a far larger challenge in these infections. CREs appear to occur more frequently than in the past but remain relatively dwarfed by the prevalence of MDR‐PA. Our data are generally in agreement with the 2009 to 2010 data from the National Healthcare Safety Network (NHSN) maintained by the CDC, which focuses on CLABSI and ventilator‐associated pneumonia (VAP) rather than general BSI and pneumonia in US hospitals.[25] In this report, the proportion of PA that were classified as MDR according to a definition similar to ours was 15.4% in CLABSI and 17.7% in VAP. In contrast, we document that 13.5% of PA causing BSI and 21.7% causing pneumonia were due to MDR‐PA organisms. This mild divergence likely reflects the slightly different antimicrobials utilized to define MDR‐PA in the 2 studies, as well as variance in the populations examined. An additional data point reported in the NHSN study is the proportion of MDR‐PA CLABSI originating in the ICU (16.8%) versus non‐ICU hospital locations (13.3%). Although the difference we found in the prevalence of BSI by the location in the hospital was greater, we confirm that ICU specimens carry a higher risk of harboring MDR‐PA.
Our study has a number of strengths and limitations. Because we used a nationally representative database to derive our estimates, our results are highly generalizable.
The TSN database consists of microbiology samples from hospital laboratories. Although we attempted to reduce the risk of duplication, because of how samples are numbered in the database, repeat sampling remains a possibility. The definitions of resistance were based on phenotypic patterns of resistance to various antimicrobial classes. This makes our resistant organisms subject to misclassification.
In summary, although carbapenem resistance among Enterobacteriaceae has emerged as an important phenomenon, multidrug resistance among PA remains relatively more prevalent in the United States. Furthermore, over the decade examined, MDR‐PA has remained an important pathogen in pneumonia and BSI that persists across all geographic regions of the United States. Although CRE is rightfully receiving a disproportionate share of attention from public health officials, it would be shortsighted to ignore the importance of MDR‐PA as a target, not only for transmission prevention and antimicrobial stewardship, but also for new therapeutic development. Because the patterns of resistance are rapidly evolving, it is incumbent upon our public health enterprise to perform more granular real‐time surveillance to allow changes in epidemiology to inform policy and treatment decisions.
ACKNOWLEDGEMENTS
Disclosures: This study was supported by a grant from Cubist Pharmaceuticals. The authors report no conflicts of interest.
Administration of initially appropriate antimicrobial therapy represents a key determinant of outcome in patients with severe infection.[1, 2, 3, 4, 5, 6, 7, 8, 9] The variable patterns of antimicrobial resistance seen between and within healthcare institutions complicate the process of antibiotic selection. Although much attention has historically focused on Staphylococcus aureus, resistance among Gram‐negative pathogens has emerged as a major challenge in the care of hospitalized, and particularly critically ill, patients.[2, 10, 11] Multidrug, and more specifically carbapenem resistance, among such common organisms as Pseudomonas aeruginosa (PA) and Enterobacteriaceae represents a major treatment challenge.[2] A recent US‐based surveillance study reported that a quarter of device‐related infections in hospitalized patients were caused by carbapenem‐resistant PA.[10]
In addition to changes in resistance patterns seen among PA isolates, increasing rates of nonsusceptibility have been described among Enterobacteriaceae. Resistance rates to third‐generation cephalosporins in these pathogens have risen steadily since 1988, reaching 20% among Klebsiella pneumoniae and 5% among Escherichia coli isolates by 2004.[11] In response to this, clinicians have increasingly utilized carbapenems to treat patients with serious Gram‐negative infections. However, the development of several types of carbapenemases by Enterobacteriaceae has led to a greater prevalence of carbapenem‐resistant Enterobacteriaceae species (CRE).[12, 13, 14, 15, 16, 17, 18] In fact, a recent report from the Centers for Disease Control and Prevention (CDC) documents a rapid rise in both the prevalence and extent of CRE in the United States.[19]
These Gram‐negative multidrug‐resistant (MDR) organisms frequently cause serious infections including pneumonia and bloodstream infections (BSI). The fact that these conditions, if not addressed in a timely and appropriate manner, lead to high morbidity, mortality, and costs, makes understanding the patterns of resistance that much more critical. To gain a better understanding of the prevalence and characteristics of MDR rates among PA and carbapenem resistance in Enterobacteriaceae in patients hospitalized in the United States with pneumonia and BSI, we conducted a multicenter survey of microbiology data.
METHODS
To determine the prevalence of predefined resistance patterns among PA and Enterobacteriaceae in pneumonia and BSI specimens, we examined The Surveillance Network (TSN) database from Eurofins between years 2000 and 2009. The database has been used extensively for surveillance purposes since 1994 and has previously been described in detail.[17, 20, 21, 22, 23] Briefly, TSN is a warehouse of routine clinical microbiology data collected from a nationally representative sample of microbiology laboratories in 217 hospitals in the United States. To minimize selection bias, laboratories are included based on their geography and the demographics of the populations they serve.[20] Only clinically significant samples are reported. No personal identifying information for source patients is available in this database. Only source laboratories that perform antimicrobial susceptibility testing according standard US Food and Drug Administration‐approved testing methods and interpret susceptibility in accordance with the Clinical Laboratory Standards Institute breakpoints are included.[24] All enrolled laboratories undergo a pre‐enrollment site visit. Logical filters are used for routine quality control to detect unusual susceptibility profiles and to ensure appropriate testing methods. Repeat testing and reporting are done as necessary.[20]
Laboratory samples are reported as susceptible, intermediate, or resistant.[24] We required that samples have susceptibility data for each of the antimicrobials needed to determine their resistance phenotype. These susceptibility patterns served as phenotypic surrogates for resistance. We grouped intermediate samples together with the resistant ones for the purposes of the current analysis. Duplicate isolates were excluded. Only samples representing 1 of the 2 infections of interest, pneumonia and BSI, were included.
We defined MDR‐PA as any isolate resistant to 3 of the following drug classes: aminoglycoside (gentamicin), antipseudomonal penicillin (piperacillin‐tazobactam), antipseudomonal cephalosporin (ceftazidime), carbapenems (imipenem, meropenem), and fluoroquinolone (ciprofloxacin). Enterobacteriaceae were considered CRE if resistant to both a third‐generation cephalosporin and a carbapenem. We examined the data by infection type, year, the 9 US Census geographical divisions, and intensive care unit (ICU) origin.
We did not pursue hypothesis testing due to a high risk of type I error in this large dataset. Therefore, only clinically important trends are highlighted.
RESULTS
Source specimen characteristics for the 205,526 PA (187,343 pneumonia and 18,183 BSI) and 95,566 Enterobacteriaceae specimens (58,810 pneumonia and 36,756 BSI) identified are presented in Table 1. The median age of the patients from which the isolates derive was similar among the PA pneumonia, Enterobacteriaceae pneumonia, and Enterobacteriaceae BSI groups, but higher in the PA BSI group. Similarly, there were differences in the gender distribution of source patients between the organisms and infections. Namely, although females represented a stable 42% of each of the infections with PA, the proportions of females with Enterobacteriaceae pneumonia (36.2%) differed from that in the BSI group (48.6%). Pneumonia specimens (34.0% PA and 39.0% Enterobacteriaceae) were more likely to originate in the ICU than those from BSI (28.4% PA and 21.1% Enterobacteriaceae).
| Pseudomonas aeruginosa, N=205,526 | Enterobacteriaceae, N=95,566 | |
|---|---|---|
| ||
| Pneumonia, n | 187,343 | 58,810 |
| Age, y, median (IQR 25, 75) | 54 (23, 71) | 55 (21, 71) |
| Gender, female, n (%) | 78,418 (41.9) | 21,305 (36.2) |
| ICU origin, n (%) | 63,755 (34.0) | 22,942 (39.0) |
| Meeting definitions of resistance, n (%) | 41,180 (22.0) | 930 (1.6) |
| BSI, n | 18,183 | 36,756 |
| Age, y, median (IQR 25, 75) | 59 (31, 75) | 55 (24, 71) |
| Gender, female, n (%) | 7,448 (41.8) | 17,871 (48.6) |
| ICU origin, n (%) | 5,170 (28.4) | 7,751 (21.1) |
| Meeting definitions of resistance, n (%) | 2,668 (14.7) | 394 (1.1) |
The prevalence of resistance among PA isolates was approximately 15‐fold higher than among Enterobacteriaceae specimens in both infection types (Table 1). This pattern persisted when stratified by infection type (pneumonia: 22.0% MDR‐PA vs 1.6% CRE; BSI: 14.7% MDR‐PA vs 1.1% CRE).
Over the time frame of the study, we detected variable patterns of resistance in the 2 groups of organisms (Figure 1). Namely, among PA in both pneumonia and BSI there was an initial rise in the proportion of MDR specimens between 2000 and 2003, followed by a stabilization until 2005, an additional rise in 2006, and a gradual decline and stabilization through 2009. These fluctuations notwithstanding, there was a net rise in MDR‐PA as a proportion of all PA from 10.7% in 2000 to 13.5% in 2009 among BSI, and from 19.2% in 2000 to 21.7% in 2009 among pneumonia specimens. Among Enterobacteriaceae, the CRE phenotype emerged in 2002 in both infection types and peaked in 2008 at 3.6% in BSI and 5.3% in pneumonia. This peak was followed by a stabilization in 2009 in BSI (3.5%) and a further decline, albeit minor, to 4.6% in pneumonia.
We noted geographic differences in the distribution of resistance (Table 2). Although MDR‐PA was more likely to originate from the East and West North Central divisions, and least likely from the New England and Mountain states, most CRE was detected in the specimens from the latter 2 regions. When stratified by ICU as the location of specimen origin, there were differences in the prevalence of resistant organisms of both types, but these differences were observed only in BSI specimens and not in pneumonia (Figure 2). That is, in BSI, the likelihood of a resistant organism originating from the ICU was approximately double that from a non‐ICU location for both MDR‐PA (21.9% vs 11.8%) and CRE (2.0% vs 0.8%).
| Census Division | MDR‐PA | CRE | ||
|---|---|---|---|---|
| BSI | Pneumonia | BSI | Pneumonia | |
| ||||
| East North Central | 20.8% | 26.9% | 2.0% | 1.9% |
| West North Central | 18.0% | 22.1% | 0.8% | 0.7% |
| East South Central | 15.8% | 20.5% | 0.1% | 0.1% |
| West South Central | 13.5% | 21.7% | 0.3% | 0.5% |
| Pacific | 13.1% | 20.3% | 0.3% | 0.3% |
| Mid‐Atlantic | 12.6% | 20.5% | 2.5% | 3.8% |
| South Atlantic | 12.6% | 21.6% | 0.9% | 1.5% |
| New England | 10.7% | 19.7% | 1.3% | 2.9% |
| Mountain | 8.5% | 19.4% | 0.4% | 1.1% |
DISCUSSION
We have demonstrated that among both pneumonia and BSI specimens, PA and Enterobacteriaceae have a high prevalence of multidrug resistance. When examined cross‐sectionally, in both pneumonia and BSI, the prevalence of MDR‐PA was approximately 15‐fold higher than the prevalence of CRE among Enterobacteriaceae. Over the time frame of the study, MDR‐PA rose and then fell and stabilized to levels only slightly higher than those observed at the beginning of the observation period. In contrast, CRE emerged and rose precipitously between 2006 and 2008, and appeared to stabilize in 2009 in both infection types. Interestingly, we observed geographic variability among resistant isolates. Specifically, the prevalence of CRE was highest in the region with a relatively low prevalence of MDR‐PA. Despite this heterogeneity geographically, resistance for both isolate types in BSI but not in pneumonia was substantially higher in the ICU than outside the ICU.
Our data enhance the current understanding of distribution of Gram‐negative resistance in the United States. A recent study by Braykov and colleagues examined time trends in the development of CRE phenotype among Klebsiella pneumoniae in the United States.[17] By focusing on this single pathogen in various infections within Eurofin's TSN database between 1999 and 2010, they pinpointed its initial emergence to year 2002, with a notably steep rise between 2006 and 2009, with some reduction in the pace of growth in 2010. We have documented an analogous rise in the CRE phenotype among all Enterobacteriaceae, particularly in pneumonia and BSI within a similar time period. Thus, our data on the 1 hand broaden the concern about this pathogen beyond just a single organism within Enterobacteraceae and a single antimicrobial class, and on the other hand serve to focus attention on 2 clinically burdensome infection types, pneumonia and BSI.
Another recent investigation reported a rise in carbapenem‐resistant Enterobacteriaceae in US hospitals over the past decade.[19] Drawing on data from multiple sources, including the dataset used for the current analysis, this study examined the patterns of single‐class resistance to carbapenems among central line‐associated BSI (CLABSI) and catheter‐associated urinary tract infection specimens. Consistent with our findings, these authors noted that the highest percentage of hospitals reporting such single‐class carbapenem‐resistant specimens were located in the Northeastern United States. They also described that the proportion of Enterobacteriaceae with single‐class carbapenem resistance rose from 0% in 2001 to 1.4% in 2010. An additional CDC analysis reported that single‐class carbapenem resistance now exists in 4.2% of Enterobacteraciae as compared to 1.2% of isolates in 2001. We confirm that this rise in single‐class resistance is echoed by a rise in the prevalence of the CRE phenotype, and provides further granularity to this problem, specifically in the setting of pneumonia and BSI.
Although CRE has become an important concern in the treatment of patients with pneumonia and BSI, MDR‐PA remains a far larger challenge in these infections. CREs appear to occur more frequently than in the past but remain relatively dwarfed by the prevalence of MDR‐PA. Our data are generally in agreement with the 2009 to 2010 data from the National Healthcare Safety Network (NHSN) maintained by the CDC, which focuses on CLABSI and ventilator‐associated pneumonia (VAP) rather than general BSI and pneumonia in US hospitals.[25] In this report, the proportion of PA that were classified as MDR according to a definition similar to ours was 15.4% in CLABSI and 17.7% in VAP. In contrast, we document that 13.5% of PA causing BSI and 21.7% causing pneumonia were due to MDR‐PA organisms. This mild divergence likely reflects the slightly different antimicrobials utilized to define MDR‐PA in the 2 studies, as well as variance in the populations examined. An additional data point reported in the NHSN study is the proportion of MDR‐PA CLABSI originating in the ICU (16.8%) versus non‐ICU hospital locations (13.3%). Although the difference we found in the prevalence of BSI by the location in the hospital was greater, we confirm that ICU specimens carry a higher risk of harboring MDR‐PA.
Our study has a number of strengths and limitations. Because we used a nationally representative database to derive our estimates, our results are highly generalizable.
The TSN database consists of microbiology samples from hospital laboratories. Although we attempted to reduce the risk of duplication, because of how samples are numbered in the database, repeat sampling remains a possibility. The definitions of resistance were based on phenotypic patterns of resistance to various antimicrobial classes. This makes our resistant organisms subject to misclassification.
In summary, although carbapenem resistance among Enterobacteriaceae has emerged as an important phenomenon, multidrug resistance among PA remains relatively more prevalent in the United States. Furthermore, over the decade examined, MDR‐PA has remained an important pathogen in pneumonia and BSI that persists across all geographic regions of the United States. Although CRE is rightfully receiving a disproportionate share of attention from public health officials, it would be shortsighted to ignore the importance of MDR‐PA as a target, not only for transmission prevention and antimicrobial stewardship, but also for new therapeutic development. Because the patterns of resistance are rapidly evolving, it is incumbent upon our public health enterprise to perform more granular real‐time surveillance to allow changes in epidemiology to inform policy and treatment decisions.
ACKNOWLEDGEMENTS
Disclosures: This study was supported by a grant from Cubist Pharmaceuticals. The authors report no conflicts of interest.
- National Nosocomial Infections Surveillance (NNIS) System Report. Am J Infect Control. 2004;32:470.
- , , , . National surveillance of antimicrobial resistance in Pseudomonas aeruginosa isolates obtained from intensive care unit patients from 1993 to 2002. Antimicrob Agents Chemother. 2004;48:4606–4610.
- , , , et al. Health care‐associated pneumonia and community‐acquired pneumonia: a single‐center experience. Antimicrob Agents Chemother. 2007;51:3568–3573.
- , , , et al. Clinical importance of delays in the initiation of appropriate antibiotic treatment for ventilator‐associated pneumonia. Chest. 2002;122:262–268.
- . Modification of empiric antibiotic treatment in patients with pneumonia acquired in the intensive care unit. ICU‐Acquired Pneumonia Study Group. Intensive Care Med. 1996;22:387–394.
- , , , , . Antimicrobial therapy escalation and hospital mortality among patients with HCAP: a single center experience. Chest. 2008:134:963–968.
- , , , et al. Surviving Sepsis Campaign: international guidelines for management of severe sepsis and septic shock: 2008. Crit Care Med. 2008;36:296–327.
- , , , , , . Inappropriate antibiotic therapy in Gram‐negative sepsis increases hospital length of stay. Crit Care Med. 2011;39:46–51.
- , , , . Inadequate antimicrobial treatment of infections: a risk factor for hospital mortality among critically ill patients. Chest. 1999;115:462–474.
- , , , et al. Antimicrobial‐resistant pathogens associated with healthcare‐associated infections: annual summary of data reported to the National Healthcare Safety Network at the Centers for Disease Control and Prevention, 2006–2007. Infect Control Hospital Epidemiol. 2008;29:996–1011.
- , ; National Nosocomial Infections Surveillance (NNIS) System. Overview of nosocomial infections caused by Gram‐negative bacilli. Clin Infect Dis. 2005;41:848–854.
- , , . The real threat of Klebsiella pneumoniae carbapenemase‐producing bacteria. Lancet Infect Dis. 2009;9:228–236.
- , , , . Household transmission of carbapenemase‐producing Klebsiella pneumoniae. Emerg Infect Dis. 2008;14:859–860.
- , , , . Isolation of imipenem‐resistant Enterobacter species: emergence of KPC‐2 carbapenemase, molecular characterization, epidemiology, and outcomes. Antimicrob Agents Chemother. 2008;52:1413–1418.
- , , , , . Outcomes of carbapenem‐resistant Klebsiella pneumoniae infection and the impact of antimicrobial and adjunctive therapies. Infect Control Hosp Epidemiol. 2008;29:1099–1106.
- , , , , , ; for the Centers for Disease Control and Prevention Epicenter Program. Emergence and rapid regional spread of Klebsiella pneumoniae carbapenemase‐producing Enterobacteriaceae. Clin Infect Dis. 2011;53:532–540.
- , , , , . Trends in resistance to carbapenems and third‐generation cephalosporins among clinical isolates of Klebsiella pneumoniae in the United States, 1999–2010. Infect Control Hosp Epidemiol. 2013;34:259–268.
- , , , . Population‐based incidence of carbapenem‐resistant Klebsiella pneumoniae along the continuum of care, Los Angeles County. Infect Control Hosp Epidemiol. 2013;34:144–150.
- Centers for Disease Control and Prevention (CDC). Vital signs: carbapenem‐resistant enterobacteriaceae. MMWR Morb Mortal Wkly Rep. 2013;62:165–170.
- , , . Antimicrobial resistance in key bloodstream bacterial isolates: electronic surveillance with the Surveillance Network Database–USA. Clin Infect Dis. 1999;29:259–263.
- , , . Community‐associated methicillin‐resistant Staphylococcus aureus in outpatients, United States, 1999–2006. Emerg Infect Dis. 2009;15:1925–1930.
- , , . Increasing resistance of Acinetobacter species to imipenem in United States hospitals, 1999–2006. Infect Control Hosp Epidemiol. 2010;31:196–197.
- , , , , . Prevalence of antimicrobial resistance in bacteria isolated from central nervous system specimens as reported by U.S. hospital laboratories from 2000 to 2002. Ann Clin Microbiol Antimicrob. 2004;3:3.
- Clinical Laboratory Standards Institute. Available at: http://www.clsi.org. Accessed July 8, 2013.
- , , , et al. Antimicrobial‐resistant pathogens associates with healthcare‐associated infections: Summary of data reported to the National Healthcare Safety Network at the Centers for Disease Control and Prevention, 2009–2010. Infect Control Hosp Epidemiol. 2013;34:1–14.
- National Nosocomial Infections Surveillance (NNIS) System Report. Am J Infect Control. 2004;32:470.
- , , , . National surveillance of antimicrobial resistance in Pseudomonas aeruginosa isolates obtained from intensive care unit patients from 1993 to 2002. Antimicrob Agents Chemother. 2004;48:4606–4610.
- , , , et al. Health care‐associated pneumonia and community‐acquired pneumonia: a single‐center experience. Antimicrob Agents Chemother. 2007;51:3568–3573.
- , , , et al. Clinical importance of delays in the initiation of appropriate antibiotic treatment for ventilator‐associated pneumonia. Chest. 2002;122:262–268.
- . Modification of empiric antibiotic treatment in patients with pneumonia acquired in the intensive care unit. ICU‐Acquired Pneumonia Study Group. Intensive Care Med. 1996;22:387–394.
- , , , , . Antimicrobial therapy escalation and hospital mortality among patients with HCAP: a single center experience. Chest. 2008:134:963–968.
- , , , et al. Surviving Sepsis Campaign: international guidelines for management of severe sepsis and septic shock: 2008. Crit Care Med. 2008;36:296–327.
- , , , , , . Inappropriate antibiotic therapy in Gram‐negative sepsis increases hospital length of stay. Crit Care Med. 2011;39:46–51.
- , , , . Inadequate antimicrobial treatment of infections: a risk factor for hospital mortality among critically ill patients. Chest. 1999;115:462–474.
- , , , et al. Antimicrobial‐resistant pathogens associated with healthcare‐associated infections: annual summary of data reported to the National Healthcare Safety Network at the Centers for Disease Control and Prevention, 2006–2007. Infect Control Hospital Epidemiol. 2008;29:996–1011.
- , ; National Nosocomial Infections Surveillance (NNIS) System. Overview of nosocomial infections caused by Gram‐negative bacilli. Clin Infect Dis. 2005;41:848–854.
- , , . The real threat of Klebsiella pneumoniae carbapenemase‐producing bacteria. Lancet Infect Dis. 2009;9:228–236.
- , , , . Household transmission of carbapenemase‐producing Klebsiella pneumoniae. Emerg Infect Dis. 2008;14:859–860.
- , , , . Isolation of imipenem‐resistant Enterobacter species: emergence of KPC‐2 carbapenemase, molecular characterization, epidemiology, and outcomes. Antimicrob Agents Chemother. 2008;52:1413–1418.
- , , , , . Outcomes of carbapenem‐resistant Klebsiella pneumoniae infection and the impact of antimicrobial and adjunctive therapies. Infect Control Hosp Epidemiol. 2008;29:1099–1106.
- , , , , , ; for the Centers for Disease Control and Prevention Epicenter Program. Emergence and rapid regional spread of Klebsiella pneumoniae carbapenemase‐producing Enterobacteriaceae. Clin Infect Dis. 2011;53:532–540.
- , , , , . Trends in resistance to carbapenems and third‐generation cephalosporins among clinical isolates of Klebsiella pneumoniae in the United States, 1999–2010. Infect Control Hosp Epidemiol. 2013;34:259–268.
- , , , . Population‐based incidence of carbapenem‐resistant Klebsiella pneumoniae along the continuum of care, Los Angeles County. Infect Control Hosp Epidemiol. 2013;34:144–150.
- Centers for Disease Control and Prevention (CDC). Vital signs: carbapenem‐resistant enterobacteriaceae. MMWR Morb Mortal Wkly Rep. 2013;62:165–170.
- , , . Antimicrobial resistance in key bloodstream bacterial isolates: electronic surveillance with the Surveillance Network Database–USA. Clin Infect Dis. 1999;29:259–263.
- , , . Community‐associated methicillin‐resistant Staphylococcus aureus in outpatients, United States, 1999–2006. Emerg Infect Dis. 2009;15:1925–1930.
- , , . Increasing resistance of Acinetobacter species to imipenem in United States hospitals, 1999–2006. Infect Control Hosp Epidemiol. 2010;31:196–197.
- , , , , . Prevalence of antimicrobial resistance in bacteria isolated from central nervous system specimens as reported by U.S. hospital laboratories from 2000 to 2002. Ann Clin Microbiol Antimicrob. 2004;3:3.
- Clinical Laboratory Standards Institute. Available at: http://www.clsi.org. Accessed July 8, 2013.
- , , , et al. Antimicrobial‐resistant pathogens associates with healthcare‐associated infections: Summary of data reported to the National Healthcare Safety Network at the Centers for Disease Control and Prevention, 2009–2010. Infect Control Hosp Epidemiol. 2013;34:1–14.
© 2013 Society of Hospital Medicine
Severe AH Among Inpatients From the ED
Chronic hypertension affects 32% of adults in the United States.1 Each year, over 20 million emergency department visits involve hypertension.2 While many studies describe the epidemiology and outcomes of chronic hypertension, less is known about severe acute hypertension (AH). Often classified as either hypertensive urgency or emergency, it receives little attention in national treatment guidelines.3 There is a limited understanding of the epidemiology of, and the relationship between, this syndrome and patient outcomes among hospitalized patients. One registry study suggested that severe AH was associated with high rates of new organ damage, need for treatment in the intensive care unit, and a 90‐day readmission rate of 10%.4 This investigation, however, lacked generalizability, because it only enrolled subjects requiring therapy with an intravenous antihypertensive agent and did not provide information on the prevalence of severe AH. Qureshi et al.5 analyzed information in a more representative sample from the national hospital ambulatory care survey, however, the only outcome examined was the correlation between acute blood pressure and stroke. Studies focusing on a selected patient population may be of limited value to hospitalists, because they are commonly required to treat a range of patients presenting to the hospital.
In light of severe syndromes that may be associated with, or complicated by, severe AH presented early during acute care, hospitalists require a better understanding of the prevalence and the relationship between severe AH and attendant mortality and morbidity. In addition, an assessment of the association of severe AH on the need for intensive care unit (ICU) admission and mechanical ventilation (MV) may aid the initial treatment assessments and triage decisions required of hospitalists.
Our objective was to describe the prevalence and implications of severe AH present at the time of evaluation in the ED among patients eventually hospitalized, using clinical data collected on all consecutive admissions across a range of clinical conditions. We sought to determine the independent contribution, if any, of severe AH to hospital mortality, need for MV on admission, as well as hospital length of stay (LOS).
METHODS
Study Design and Setting
This was a retrospective analysis of adults admitted to 114 acute‐care hospitals in the United States from 2005 through 2007. The New England Institutional Review Board/Human Subjects Research Committee (Wellesley, MA) reviewed and approved this study. It was conducted in compliance with the Health Insurance Portability and Accountability Act (HIPAA).
Data
Data were obtained from one of the Clinical Research Databases from CareFusion (formerly Cardinal Health [CareFusion Clinical Research Services, Marlborough, MA]).613 The database contains electronically imported or manually extracted demographic, clinical (eg, comorbidities, vital signs, laboratory values, other clinical findings), and administrative data (eg, diagnosis, procedures, and length of hospitalization). All vital signs were manually extracted, including the highest and the lowest ED systolic blood pressure (SBP) measurements during the ED stay, but before inpatient admission. Patients admitted for childbirth or mental health reasons were not included.
Patients
Our main analysis focused on patients whose highest and lowest SBP were collected in the ED. These patients accounted for approximately 90% of all inpatients admitted through the ED. For the approximately 9% of patients who had only 1 SBP collected, we conducted a sensitivity analysis by both including and excluding them in the main analysis to determine if the absence of a second SBP measurement altered our findings. Patients were grouped into 1 of the following 8 mutually exclusive strata based on the maximum SBP (mmHg) in the ED: <100, 101‐139, 140‐180, 181‐190, 191‐200, 201‐210, 211‐220, and >220.
Measures
The primary exposure of interest was the prevalence of severe AH, defined as at least 1 SBP measurement recorded in the ED in excess of 180 mmHg. Outcome measures included in‐hospital mortality, need for MV on admission day (defined by International Classification of Diseases, 9th revision, Clinical Modification [ICD‐9‐CM] procedure codes of 96.70, 96.71, and 96.72), and LOS. We stratified these outcomes for each inpatient admission according to 1 of 112 mutually exclusive groups of principal diagnoses (see Supporting Appendix A in the online version of this article). To simplify the presentations, we pooled the groups into 9 major disease categories based on organ systems.
Primary Data Analysis
All statistical analyses were performed using Statistical Analysis Software (SAS version 9.01; SAS Institute Inc, Cary, NC). For evaluating trending, we used the Cochran‐Armitage test for dichotomous variables (mortality and MV), and linear regression for continuous variable (LOS). We employed a logistic regression model to estimate risk of mortality and need for MV on admission. We used linear regression models to estimate the LOS associated with severe AH. We modeled each outcome as a function of patient disease severity and SBP strata. Because patients with the most severe AH tended to have higher mortality early in hospitalization, our analysis of LOS was limited to patients who survived index hospitalization.
The original disease‐specific risk‐adjustment models accounting for patient‐level confounding risk factors, including demographics, physiologic presentation on admission (vital signs, altered mental status, and laboratory findings), and chronic conditions, were previously developed and validated.12, 13 We recalibrated each of the 112 models, for the current study cohort, using the logit of predicted probability of death generated from the mortality risk‐adjustment model as a propensity score of disease severity. Using this propensity score as an aggregate severity adjuster, we refit 9 logistic regression models (1 for each major disease category) to estimate the odds ratios for mortality or need for MV for each of the 7 SBP strata in the regression models with 101‐139 mmHg as the reference group. To estimate the attributable LOS (if any) of severe AH among survivors, we fit the 9 LOS models using log transformed LOS (to normalize the potentially skewed distribution of LOS) as the outcome, controlling for disease severity. The attributable LOS and 95% confidence intervals (CI) were estimated from 1000 bootstrap iterations, with the median as the parameter estimate and 2.5th and 97.5th percentile as 95% CI.14, 15
Sensitivity Analysis
To address potential bias of LOS associated with inpatient mortality, we refit 9 LOS models, including both patients who died and those who survived the index hospitalization. The models adjusted for disease severity, mortality, and the interaction of severity and mortality. Because patients with only 1 SBP recorded at ED may be different from those with more than 1 SBP recorded, we conducted analysis by adding these patients in the study cohort to examine the potential change of overall prevalence of AH and associated mortality for the study population.
To address the potential for a center‐specific effect on outcomes, we refit all the models using a mixed model approach.16 The mixed model accounts for both patient‐level risk factors and hospital‐specific effects on the observed outcomes.
RESULTS
Patient Characteristics
The study cohort was comprised of 1,290,804 adults who were admitted through the ED, from 2005 through 2007, and whose highest and lowest SBP measurements were collected in the ED. Median age was 69 years (interquartile range, 53‐81) for the overall population. Median age was 74 (interquartile range, 60‐83) for patients with severe AH (Table 1). Hospital mortality was 3.6% (n = 46,033), with 6119 (13.3%) having severe AH.
| Characteristic | Prevalence, n (Column %) | Severe Acute Hypertension, n (Row %) |
|---|---|---|
| Total number of discharges | 1,290,804 (100.0) | 178,197 (13.8) |
| Mortality | 46,033 (3.6) | 6,119 (13.3) |
| Live discharges | 1,244,771 (96.4) | 172,078 (13.8) |
| Mechanical ventilation on admission | 39,238 (3.0) | 9,508 (24.2) |
| Demographics | ||
| Age, median (1st, 3rd quartiles) | 69 (53, 81) | 74 (60, 83) |
| Male | 587,553 (45.5) | 71,085 (12.1) |
| Female | 703,244 (54.5) | 107,109 (15.2) |
| Race | ||
| White | 949,869 (73.6) | 121,930 (12.8) |
| Black | 220,601 (17.1) | 39,667 (18.0) |
| Other | 120,334 (9.3) | 16,600 (13.8) |
| Insurance | ||
| Medicare | 668,420 (51.8) | 105,078 (15.7) |
| Medicaid | 108,538 (8.4) | 12,259 (11.3) |
| Commercial | 163,858 (12.7) | 18,669 (11.4) |
| Other | 349,988 (27.1) | 42,191 (12.1) |
| Disease system by the principal diagnosis | ||
| Nervous system | 76,744 (5.9) | 22,270 (29.0) |
| Respiratory system | 222,329 (17.2) | 24,678 (11.1) |
| Circulatory system | 416,847 (32.3) | 66,852 (16.0) |
| Digestive system | 186,282 (14.4) | 17,817 (9.6) |
| Hepatobiliary/pancreas system | 52,293 (4.1) | 5,664 (10.8) |
| Endocrine system | 45,050 (3.5) | 6,625 (14.7) |
| Kidney/urinary system | 81,782 (6.3) | 11,050 (13.5) |
| Infectious diseases | 60,353 (4.7) | 4,162 (6.9) |
| Other | 149,124 (11.6) | 19,079 (12.8) |
| Comorbidity by secondary diagnoses | ||
| Hypertension | 729,417 (56.5) | 135,498 (18.6) |
| Fluid and electrolyte disorders | 306,666 (23.8) | 37,836 (12.3) |
| Diabetes without chronic complications | 286,912 (22.2) | 47,979 (16.7) |
| Chronic pulmonary disease | 283,895 (22.0) | 35,977 (12.7) |
| Congestive heart failure | 213,523 (16.5) | 33,956 (15.9) |
| Deficiency anemias | 210,230 (16.3) | 30,266 (14.4) |
| Renal failure | 159,409 (12.3) | 31,984 (20.1) |
| Hypothyroidism | 153,911 (11.9) | 22,441 (14.6) |
| Valvular disease | 140,820 (10.9) | 21,453 (15.2) |
| Depression | 137,259 (10.6) | 16,886 (12.3) |
| Other neurological disorders | 126,954 (9.8) | 19,103 (15.0) |
| Peripheral vascular disease | 88,321 (6.8) | 16,180 (18.3) |
| Obesity | 84,000 (6.5) | 12,351 (14.7) |
| Diabetes with chronic complications | 65,989 (5.1) | 13,093 (19.8) |
| Psychoses | 54,769 (4.2) | 5,555 (10.1) |
| Alcohol abuse | 51,765 (4.0) | 6,014 (11.6) |
| Pulmonary circulation disease | 49,248 (3.8) | 7,128 (14.5) |
| Coagulopathy | 43,584 (3.4) | 4,339 (10.0) |
| Paralysis | 42,128 (3.3) | 8,125 (19.3) |
| Drug abuse | 36,134 (2.8) | 4,779 (13.2) |
| Liver disease | 36,094 (2.8) | 3,218 (8.9) |
| Weight loss | 35,795 (2.8) | 3,726 (10.4) |
| Metastatic cancer | 33,517 (2.6) | 2,498 (7.5) |
| Rheumatoid arthritis | 32,545 (2.5) | 4,300 (13.2) |
| Solid tumor without metastasis | 30,677 (2.4) | 3,035 (9.9) |
| Chronic blood loss anemia | 25,416 (2.0) | 2,268 (8.9) |
| Lymphoma | 9,972 (0.8) | 871 (8.7) |
| Acquired immune deficiency syndrome | 3,048 (0.2) | 307 (10.1) |
| Peptic ulcer disease | 915 (0.1) | 131 (14.3) |
| Discharges by hospital characteristics | ||
| Teaching status | ||
| Teaching hospitals | 899,786 (69.7) | 127,512 (14.2) |
| Nonteaching hospitals | 391,018 (30.3) | 50,685 (13.0) |
| Urban status | ||
| Urban hospitals | 1,164,802 (90.2) | 162,399 (13.9) |
| Rural hospitals | 126,002 (9.8) | 15,798 (12.5) |
| Bed size | ||
| Beds <100 | 36,624 (2.8) | 4,965 (13.6) |
| Beds 100‐300 | 623,327 (48.3) | 80,156 (12.9) |
| Beds >300 | 630,853 (48.9) | 93,076 (14.8) |
Prevalence of Acute Hypertension
A total of 763,634 (59.2%) patients had at least 1 SBP measurement of 140 mmHg during the ED stay, including 178,197 (13.8%) with SBP >180 mmHg. Body systems associated with the highest prevalence of severe AH (SBP >180 mmHg) were nervous (29.0%), circulatory (16.0%), endocrine (14.7%), and kidney/urinary (13.5%) (Figure 1 presents the data in graphic form; Supporting Appendix B, in the online version of this article, presents corresponding data in table form).
Mortality
Univariable analysis revealed a graded relationship between SBP stratum and mortality risk (Figure 2a; and see Supporting Appendix C in the online version of this article). This relationship was most pronounced for nervous system diseases; mortality rates for each 10 mmHg increase in SBP from 180 to >220 mmHg were 6.5%, 8.1%, 10.0%, 12.0%, and 19.7%, respectively (trending P < 0.0001). The risk‐adjusted increase in mortality odds ratio ranged from 1.04 (95% CI: 0.89, 1.21) to 1.44 (95% CI: 1.25, 1.67) for patients in the severe AH strata compared to patients with SBP of 101 to 139 mmHg (Figure 3). Severe AH was not an independent mortality predictor in other disease categories.
Mechanical Ventilation on Admission
Univariable analysis revealed a graded relationship between severe AH and a need for MV on admission, especially for respiratory, circulatory, and infectious conditions (trending P < 0.0001) (Figure 2b; and see Supporting Appendix C in the online version of this article). In the multivariable analysis, there was a relationship between severe AH stratum and adjusted risk for MV on admission across nearly all disease categories (Figure 4).
Length of Stay
Univariable analysis revealed a graded relationship between severe AH strata and LOS for nearly all disease categories in survivors (trending P < 0.0001), except for digestive, kidney, and infectious diseases (Figure 2c; and see Supporting Appendix C in the online version of this article). For patients with nervous system diseases, the unadjusted LOS for each 10 mmHg increase in SBP from 180 to >220 was 5.8, 6.1, 6.4, 6.8, and 8.0 days, respectively (trending P < 0.0001). The relationship was similar for other disease categories which showed significant trending.
In the multivariable analysis, the relationship between severe AH strata and adjusted attributable LOS was graded across most disease categories, especially nervous, circulatory, and hepatobiliary diseases (Figure 5). The total adjusted number of hospital days attributable to severe AH was 0.43 days per case for all survivors with severe AH.
Sensitivity Analysis
Our sensitivity analysis of the LOS estimate, including those patients who died in the hospital, yielded similar findings in attributable LOS due to severe AH. In addition, when we added patients with only 1 SBP documented in the ED to the study cohort, the severe AH prevalence changed negligibly from 13.8% to 13.0%, and the associated mortality remained unchanged at 3.4%. Models using the mix model approach, which take into account hospital‐specific effects, showed similar results.
DISCUSSION
This large‐scale analysis demonstrated that severe AH was present in 13.8% of inpatients admitted through the ED. The prevalence of severe AH varied based on the primary reason for an acute care admission, ranging from 7% in infectious syndromes to nearly 30% in acute neurologic processes. Specific to patients with neurologic disease, initial severe blood pressure elevations independently correlated with mortality. Severe blood pressure elevations at ED were independently associated with an increased need for MV on admission and a prolonged LOS across a range of disease states.
Prior work on hypertension at admission has generally included single‐center analyses or only focused on patients with specific admitting diagnoses. For example, in a single ED analysis, Tilman et al.17 reported that 16% of 10,000 patients presented with elevated blood pressure (140/90 mmHg). In a multicenter review of 7000 persons, Karras et al.18, 19 described 423 patients with severe AH (180/110 mmHg) who comprised 6% of patients seen in the ED during a 1‐week period. Qureshi et al.5 noted severe AH in 13% of patients with acute stroke. While other ED‐based studies examined all ED patients, including those admitted and discharged from the ED, our study focused on those requiring hospitalization. This disparity in illness severity may, in part, explain the higher prevalence of severe AH we noted relative to others.
We further found that the prevalence of severe AH varied based on admitting diagnosis. This difference in prevalence rates by condition seems clinically plausible. Recognition of this pattern may prove valuable to hospitalists who, by the nature of their responsibilities, will encounter a broad range of patients. Because our data were derived from the largest analysis of blood pressure assessments for ED patients who were eventually hospitalized, and encompassed a multiplicity of hospitals, our findings are likely generalizable. Moreover, our large sample size enabled us to examine severe AH at each 10 mmHg increment across a variety of disease states, rather than restricting our analysis to 1 admitting diagnosis.
The independent relationship between severe blood pressure elevation and mortality was detected only in those with neurologic conditions. Incremental increases in SBP beyond 180 mmHg were associated with a stepwise escalation in the risk for death. Although recognition of the importance of blood pressure management in both ischemic and hemorrhagic stroke remains a cornerstone of therapy for these diseases, the stepwise relationship between escalating blood pressure and outcome suggests that further study is needed to determine the optimal management of severe AH among these patients. This relationship, along with the independent association between severe AH at presentation and the need for MV, underscores the importance of severe AH in critical care, representing a major challenge for intensivists and hospitalists, particularly those who practice in neurologic ICUs.
The independent association of severe AH and prolonged LOS represents a novel finding. Few reports have correlated the initial blood pressure with measures of resource use. Katz et al.4 found a median LOS of 6 days among 1000 patients who presented with severe AH and end organ dysfunction in 25 US hospitals. These investigators, however, did not explore the incremental independent contribution of initial blood pressure to LOS. Biologically, severe hypertension may exacerbate both acute and chronic conditions, thus complicating their management and resulting in longer hospitalizations.
Our analysis has limitations. While exposure misclassification is a potential concern, unlike other population‐based studies that use ICD‐9‐CM codes to identify AH cases, we relied on actual measures of blood pressure to identify subjects, thus minimizing this threat to validity. Similarly, since our end pointsmortality, MV on admission, and LOSwere also objective measures, the probability of their misclassification is minimal.
Another concerning contributor to exposure misclassification is the possibility that, in some instances, the initial elevation in BP meeting the inclusion criteria in our study cohort does not reflect the true BP. Indeed, a substantial body of research about BP measurement in the ED suggests that we may have included some persons who likely did not have AH. For example, Pitts and Adams described a regression to the mean phenomenon, with serial BP measurements in the ED, wherein the BP fell by approximately 11 mmHg over 4 hours.20 Baumann and colleagues reported a similar pattern.21 However, the findings of these 2 analyses do not necessarily apply to our study population; we focused on patients admitted to the hospital with an acute condition, while Pitts and Adams20 and Baumann et al.21 examined all ED patients. This distinction is crucial in that patients not admitted are likely less severely ill and systematically different from those who do merit hospitalization. Moreover, both Pitts and Adams20 and Cienki et al.22 observed that the regression to the mean and fall in serial BP measurements were less pronounced in those with the most extensive BP elevations. In our study, we found the strongest relationship between adverse outcomes and BP in patients with the most extreme BP elevations. Thus, misclassification is perhaps less likely to be an issue for these subjects.
In addition, misclassification may result when initially elevated BP simply represents the impact of untreated pain or anxiety in ED patients. However, Backer et al.23 and Tanabe and colleagues24 specifically explored the impact of pain and anxiety on BP in ED subjects, and neither group found a correlation between BP and either acute pain and/or anxiety scores. Our difficulties with case definitions and BP measurements for severe AH demonstrate the need for the creation and adoption of a formal, systematic approach to this syndrome, along with the need for prospective analyses to confirm our findings.
Selection bias represents a second potential threat to validity in our observational study, although this bias is mitigated by including all consecutive acute inpatient admissions to the participating hospitals. Furthermore, inclusion of the 9% of patients who had only 1 ED measurement of SBP collected did not alter the estimate of severe AH prevalence or associated outcomes.
Third, confounding may introduce the potential for false associations derived from observational data. The large sample size of our cohort allowed us to address this concern by adjusting for a large array of confounders. In addition, unlike other large‐scale population‐based studies which typically rely on administrative ICD‐9‐CM codes for risk adjustment, our analysis incorporated actual physiologic and laboratory results measured on admission, as well as a validated severity‐of‐illness scoring system for risk adjustment.12, 13
Although both SBP and diastolic blood pressure (DBP) thresholds are included in traditional definitions of hypertension, selecting SBP as the primary measure is reasonable because SBP >180 mmHg is a more important risk factor for cardiovascular disease than elevated DBP.25 Previous studies reported the relationship between the trend of SBP over time and clinical outcomes,26, 27 but we were not able to investigate the relationship of SBP trend and outcomes because the BP measurements in the our study were not collected in predefined intervals.
It would be ideal if serial blood pressure measures were to be collected at pre‐specified intervals and if more sophisticated schemas were to be used to refine the AH definition. This type of study may be possible in the future when vital signs can be collected automatically with advanced technology. Likewise, electronically captured treatment data could further help researchers to study the impact of process‐of‐care variables, including medications and other management strategies, in relation to outcomes. Finally, outpatient management of chronic hypertension is an integral part of clinical management. Unfortunately, these types of data are not available in our existing database. These limitations notwithstanding, an in‐depth understanding of the association between severe AH and potential adverse clinical and economic outcomes may direct further research in this field.
CONCLUSION
Severe AH appears common and its prevalence varies by underlying clinical condition in patients admitted from the ED. In those with acute neurologic syndromes, the degree of blood pressure elevation correlated with mortality, need for MV, and longer LOS. For many other conditions, elevation of blood pressure appeared to be linked to an increased need for MV and a prolongation in LOS. Future studies are needed to examine the potential impact of both 1) improved long‐term outpatient BP management, and 2) optimal management of severe AH upon admission on improving outcomes of patients hospitalized from the ED with severe AH.
- Health, United States, 2010.Hyattsville, MD:Centers for Disease Control and Prevention, National Center for Health Statistics;2011.
- Agency for Healthcare Research and Quality.Healthcare Cost and Utilization Project (HCUP).Rockville, MD:US Department of Health 289(19):2560–2572.
- ,,, et al.Practice patterns, outcomes, and end‐organ dysfunction for patients with acute severe hypertension: the Studying the Treatment of Acute hyperTension (STAT) registry.Am Heart J.2009;158(4):599–606.e1.
- ,,, et al.Prevalence of elevated blood pressure in 563,704 adult patients with stroke presenting to the ED in the United States.Am J Emerg Med.2007;25(1):32–38.
- ,,, et al.A prediction rule to identify low‐risk patients with community‐acquired pneumonia.N Engl J Med.1997;336(4):243–250.
- ,.A clinical assessment of MedisGroups.JAMA.1988;260(21):3159–3163.
- ,,,,,.Healthcare‐associated bloodstream infection: a distinct entity? Insights from a large U.S. database.Crit Care Med.2006;34(10):2588–2595.
- ,,,,,.Epidemiology and outcomes of health‐care‐associated pneumonia: results from a large US database of culture‐positive pneumonia.Chest.2005;128(6):3854–3862.
- ,,,,,.Morbidity and cost burden of methicillin‐resistant Staphylococcus aureus in early onset ventilator‐associated pneumonia.Crit Care.2006;10(3):R97.
- ,,,,.Evaluation of the complication rate as a measure of quality of care in coronary artery bypass graft surgery.JAMA.1995;274(4):317–323.
- ,,.Using automated clinical data for risk adjustment: development and validation of six disease‐specific mortality predictive models for pay‐for‐performance.Med Care.2007;45(8):789–805.
- ,,,,.Development and validation of a disease‐specific risk adjustment system using automated clinical data.Health Service Research.2010;45:1815–1835.
- ,.Estimating marginal and incremental effects on health outcomes using flexible link and variance function models.Biostatistics.2005;6(1):93–109.
- ,.An Introduction to the Bootstrap.London, England:Chapman 1993.
- ,.Applied Mixed Models in Medicine.2nd ed.Chichester, England:Wiley;2006.
- ,,,,,.Recognizing asymptomatic elevated blood pressure in ED patients: how good (bad) are we?Am J Emerg Med.2007;25(3):313–317.
- ,,, et al.Evaluation and treatment of patients with severely elevated blood pressure in academic emergency departments: a multicenter study.Ann Emerg Med.2006;47(3):230–236.
- ,,, et al.Elevated blood pressure in urban emergency department patients.Acad Emerg Med.2005;12(9):835–843.
- ,.Emergency department hypertension and regression to the mean.Ann Emerg Med.1998;31(2):214–218.
- ,,,.Differing prevalence estimates of elevated blood pressure in ED patients using 4 methods of categorization.Am J Emerg Med.2008;26(5):561–565.
- ,,.The validity of emergency department triage blood pressure measurements.Acad Emerg Med.2004;11(3):237–243.
- ,,.Reproducibility of increased blood pressure during an emergency department or urgent care visit.Ann Emerg Med.2003;41(4):507–512.
- ,,,,,.Increased blood pressure in the emergency department: pain, anxiety, or undiagnosed hypertension?Ann Emerg Med.2008;51(3):221–229.
- ,,.Clinical Advisory Statement. Importance of systolic blood pressure in older Americans.Hypertension.2000;35(5):1021–1024.
- ,,,.High blood pressure in early acute stroke: a sign of a poor outcome?J Hypertens.2006;24(2):381–386.
- ,,,,.Blood pressure as an independent prognostic factor in acute ischemic stroke.Can J Neurol Sci.2006;33(1):34–38.
Chronic hypertension affects 32% of adults in the United States.1 Each year, over 20 million emergency department visits involve hypertension.2 While many studies describe the epidemiology and outcomes of chronic hypertension, less is known about severe acute hypertension (AH). Often classified as either hypertensive urgency or emergency, it receives little attention in national treatment guidelines.3 There is a limited understanding of the epidemiology of, and the relationship between, this syndrome and patient outcomes among hospitalized patients. One registry study suggested that severe AH was associated with high rates of new organ damage, need for treatment in the intensive care unit, and a 90‐day readmission rate of 10%.4 This investigation, however, lacked generalizability, because it only enrolled subjects requiring therapy with an intravenous antihypertensive agent and did not provide information on the prevalence of severe AH. Qureshi et al.5 analyzed information in a more representative sample from the national hospital ambulatory care survey, however, the only outcome examined was the correlation between acute blood pressure and stroke. Studies focusing on a selected patient population may be of limited value to hospitalists, because they are commonly required to treat a range of patients presenting to the hospital.
In light of severe syndromes that may be associated with, or complicated by, severe AH presented early during acute care, hospitalists require a better understanding of the prevalence and the relationship between severe AH and attendant mortality and morbidity. In addition, an assessment of the association of severe AH on the need for intensive care unit (ICU) admission and mechanical ventilation (MV) may aid the initial treatment assessments and triage decisions required of hospitalists.
Our objective was to describe the prevalence and implications of severe AH present at the time of evaluation in the ED among patients eventually hospitalized, using clinical data collected on all consecutive admissions across a range of clinical conditions. We sought to determine the independent contribution, if any, of severe AH to hospital mortality, need for MV on admission, as well as hospital length of stay (LOS).
METHODS
Study Design and Setting
This was a retrospective analysis of adults admitted to 114 acute‐care hospitals in the United States from 2005 through 2007. The New England Institutional Review Board/Human Subjects Research Committee (Wellesley, MA) reviewed and approved this study. It was conducted in compliance with the Health Insurance Portability and Accountability Act (HIPAA).
Data
Data were obtained from one of the Clinical Research Databases from CareFusion (formerly Cardinal Health [CareFusion Clinical Research Services, Marlborough, MA]).613 The database contains electronically imported or manually extracted demographic, clinical (eg, comorbidities, vital signs, laboratory values, other clinical findings), and administrative data (eg, diagnosis, procedures, and length of hospitalization). All vital signs were manually extracted, including the highest and the lowest ED systolic blood pressure (SBP) measurements during the ED stay, but before inpatient admission. Patients admitted for childbirth or mental health reasons were not included.
Patients
Our main analysis focused on patients whose highest and lowest SBP were collected in the ED. These patients accounted for approximately 90% of all inpatients admitted through the ED. For the approximately 9% of patients who had only 1 SBP collected, we conducted a sensitivity analysis by both including and excluding them in the main analysis to determine if the absence of a second SBP measurement altered our findings. Patients were grouped into 1 of the following 8 mutually exclusive strata based on the maximum SBP (mmHg) in the ED: <100, 101‐139, 140‐180, 181‐190, 191‐200, 201‐210, 211‐220, and >220.
Measures
The primary exposure of interest was the prevalence of severe AH, defined as at least 1 SBP measurement recorded in the ED in excess of 180 mmHg. Outcome measures included in‐hospital mortality, need for MV on admission day (defined by International Classification of Diseases, 9th revision, Clinical Modification [ICD‐9‐CM] procedure codes of 96.70, 96.71, and 96.72), and LOS. We stratified these outcomes for each inpatient admission according to 1 of 112 mutually exclusive groups of principal diagnoses (see Supporting Appendix A in the online version of this article). To simplify the presentations, we pooled the groups into 9 major disease categories based on organ systems.
Primary Data Analysis
All statistical analyses were performed using Statistical Analysis Software (SAS version 9.01; SAS Institute Inc, Cary, NC). For evaluating trending, we used the Cochran‐Armitage test for dichotomous variables (mortality and MV), and linear regression for continuous variable (LOS). We employed a logistic regression model to estimate risk of mortality and need for MV on admission. We used linear regression models to estimate the LOS associated with severe AH. We modeled each outcome as a function of patient disease severity and SBP strata. Because patients with the most severe AH tended to have higher mortality early in hospitalization, our analysis of LOS was limited to patients who survived index hospitalization.
The original disease‐specific risk‐adjustment models accounting for patient‐level confounding risk factors, including demographics, physiologic presentation on admission (vital signs, altered mental status, and laboratory findings), and chronic conditions, were previously developed and validated.12, 13 We recalibrated each of the 112 models, for the current study cohort, using the logit of predicted probability of death generated from the mortality risk‐adjustment model as a propensity score of disease severity. Using this propensity score as an aggregate severity adjuster, we refit 9 logistic regression models (1 for each major disease category) to estimate the odds ratios for mortality or need for MV for each of the 7 SBP strata in the regression models with 101‐139 mmHg as the reference group. To estimate the attributable LOS (if any) of severe AH among survivors, we fit the 9 LOS models using log transformed LOS (to normalize the potentially skewed distribution of LOS) as the outcome, controlling for disease severity. The attributable LOS and 95% confidence intervals (CI) were estimated from 1000 bootstrap iterations, with the median as the parameter estimate and 2.5th and 97.5th percentile as 95% CI.14, 15
Sensitivity Analysis
To address potential bias of LOS associated with inpatient mortality, we refit 9 LOS models, including both patients who died and those who survived the index hospitalization. The models adjusted for disease severity, mortality, and the interaction of severity and mortality. Because patients with only 1 SBP recorded at ED may be different from those with more than 1 SBP recorded, we conducted analysis by adding these patients in the study cohort to examine the potential change of overall prevalence of AH and associated mortality for the study population.
To address the potential for a center‐specific effect on outcomes, we refit all the models using a mixed model approach.16 The mixed model accounts for both patient‐level risk factors and hospital‐specific effects on the observed outcomes.
RESULTS
Patient Characteristics
The study cohort was comprised of 1,290,804 adults who were admitted through the ED, from 2005 through 2007, and whose highest and lowest SBP measurements were collected in the ED. Median age was 69 years (interquartile range, 53‐81) for the overall population. Median age was 74 (interquartile range, 60‐83) for patients with severe AH (Table 1). Hospital mortality was 3.6% (n = 46,033), with 6119 (13.3%) having severe AH.
| Characteristic | Prevalence, n (Column %) | Severe Acute Hypertension, n (Row %) |
|---|---|---|
| Total number of discharges | 1,290,804 (100.0) | 178,197 (13.8) |
| Mortality | 46,033 (3.6) | 6,119 (13.3) |
| Live discharges | 1,244,771 (96.4) | 172,078 (13.8) |
| Mechanical ventilation on admission | 39,238 (3.0) | 9,508 (24.2) |
| Demographics | ||
| Age, median (1st, 3rd quartiles) | 69 (53, 81) | 74 (60, 83) |
| Male | 587,553 (45.5) | 71,085 (12.1) |
| Female | 703,244 (54.5) | 107,109 (15.2) |
| Race | ||
| White | 949,869 (73.6) | 121,930 (12.8) |
| Black | 220,601 (17.1) | 39,667 (18.0) |
| Other | 120,334 (9.3) | 16,600 (13.8) |
| Insurance | ||
| Medicare | 668,420 (51.8) | 105,078 (15.7) |
| Medicaid | 108,538 (8.4) | 12,259 (11.3) |
| Commercial | 163,858 (12.7) | 18,669 (11.4) |
| Other | 349,988 (27.1) | 42,191 (12.1) |
| Disease system by the principal diagnosis | ||
| Nervous system | 76,744 (5.9) | 22,270 (29.0) |
| Respiratory system | 222,329 (17.2) | 24,678 (11.1) |
| Circulatory system | 416,847 (32.3) | 66,852 (16.0) |
| Digestive system | 186,282 (14.4) | 17,817 (9.6) |
| Hepatobiliary/pancreas system | 52,293 (4.1) | 5,664 (10.8) |
| Endocrine system | 45,050 (3.5) | 6,625 (14.7) |
| Kidney/urinary system | 81,782 (6.3) | 11,050 (13.5) |
| Infectious diseases | 60,353 (4.7) | 4,162 (6.9) |
| Other | 149,124 (11.6) | 19,079 (12.8) |
| Comorbidity by secondary diagnoses | ||
| Hypertension | 729,417 (56.5) | 135,498 (18.6) |
| Fluid and electrolyte disorders | 306,666 (23.8) | 37,836 (12.3) |
| Diabetes without chronic complications | 286,912 (22.2) | 47,979 (16.7) |
| Chronic pulmonary disease | 283,895 (22.0) | 35,977 (12.7) |
| Congestive heart failure | 213,523 (16.5) | 33,956 (15.9) |
| Deficiency anemias | 210,230 (16.3) | 30,266 (14.4) |
| Renal failure | 159,409 (12.3) | 31,984 (20.1) |
| Hypothyroidism | 153,911 (11.9) | 22,441 (14.6) |
| Valvular disease | 140,820 (10.9) | 21,453 (15.2) |
| Depression | 137,259 (10.6) | 16,886 (12.3) |
| Other neurological disorders | 126,954 (9.8) | 19,103 (15.0) |
| Peripheral vascular disease | 88,321 (6.8) | 16,180 (18.3) |
| Obesity | 84,000 (6.5) | 12,351 (14.7) |
| Diabetes with chronic complications | 65,989 (5.1) | 13,093 (19.8) |
| Psychoses | 54,769 (4.2) | 5,555 (10.1) |
| Alcohol abuse | 51,765 (4.0) | 6,014 (11.6) |
| Pulmonary circulation disease | 49,248 (3.8) | 7,128 (14.5) |
| Coagulopathy | 43,584 (3.4) | 4,339 (10.0) |
| Paralysis | 42,128 (3.3) | 8,125 (19.3) |
| Drug abuse | 36,134 (2.8) | 4,779 (13.2) |
| Liver disease | 36,094 (2.8) | 3,218 (8.9) |
| Weight loss | 35,795 (2.8) | 3,726 (10.4) |
| Metastatic cancer | 33,517 (2.6) | 2,498 (7.5) |
| Rheumatoid arthritis | 32,545 (2.5) | 4,300 (13.2) |
| Solid tumor without metastasis | 30,677 (2.4) | 3,035 (9.9) |
| Chronic blood loss anemia | 25,416 (2.0) | 2,268 (8.9) |
| Lymphoma | 9,972 (0.8) | 871 (8.7) |
| Acquired immune deficiency syndrome | 3,048 (0.2) | 307 (10.1) |
| Peptic ulcer disease | 915 (0.1) | 131 (14.3) |
| Discharges by hospital characteristics | ||
| Teaching status | ||
| Teaching hospitals | 899,786 (69.7) | 127,512 (14.2) |
| Nonteaching hospitals | 391,018 (30.3) | 50,685 (13.0) |
| Urban status | ||
| Urban hospitals | 1,164,802 (90.2) | 162,399 (13.9) |
| Rural hospitals | 126,002 (9.8) | 15,798 (12.5) |
| Bed size | ||
| Beds <100 | 36,624 (2.8) | 4,965 (13.6) |
| Beds 100‐300 | 623,327 (48.3) | 80,156 (12.9) |
| Beds >300 | 630,853 (48.9) | 93,076 (14.8) |
Prevalence of Acute Hypertension
A total of 763,634 (59.2%) patients had at least 1 SBP measurement of 140 mmHg during the ED stay, including 178,197 (13.8%) with SBP >180 mmHg. Body systems associated with the highest prevalence of severe AH (SBP >180 mmHg) were nervous (29.0%), circulatory (16.0%), endocrine (14.7%), and kidney/urinary (13.5%) (Figure 1 presents the data in graphic form; Supporting Appendix B, in the online version of this article, presents corresponding data in table form).
Mortality
Univariable analysis revealed a graded relationship between SBP stratum and mortality risk (Figure 2a; and see Supporting Appendix C in the online version of this article). This relationship was most pronounced for nervous system diseases; mortality rates for each 10 mmHg increase in SBP from 180 to >220 mmHg were 6.5%, 8.1%, 10.0%, 12.0%, and 19.7%, respectively (trending P < 0.0001). The risk‐adjusted increase in mortality odds ratio ranged from 1.04 (95% CI: 0.89, 1.21) to 1.44 (95% CI: 1.25, 1.67) for patients in the severe AH strata compared to patients with SBP of 101 to 139 mmHg (Figure 3). Severe AH was not an independent mortality predictor in other disease categories.
Mechanical Ventilation on Admission
Univariable analysis revealed a graded relationship between severe AH and a need for MV on admission, especially for respiratory, circulatory, and infectious conditions (trending P < 0.0001) (Figure 2b; and see Supporting Appendix C in the online version of this article). In the multivariable analysis, there was a relationship between severe AH stratum and adjusted risk for MV on admission across nearly all disease categories (Figure 4).
Length of Stay
Univariable analysis revealed a graded relationship between severe AH strata and LOS for nearly all disease categories in survivors (trending P < 0.0001), except for digestive, kidney, and infectious diseases (Figure 2c; and see Supporting Appendix C in the online version of this article). For patients with nervous system diseases, the unadjusted LOS for each 10 mmHg increase in SBP from 180 to >220 was 5.8, 6.1, 6.4, 6.8, and 8.0 days, respectively (trending P < 0.0001). The relationship was similar for other disease categories which showed significant trending.
In the multivariable analysis, the relationship between severe AH strata and adjusted attributable LOS was graded across most disease categories, especially nervous, circulatory, and hepatobiliary diseases (Figure 5). The total adjusted number of hospital days attributable to severe AH was 0.43 days per case for all survivors with severe AH.
Sensitivity Analysis
Our sensitivity analysis of the LOS estimate, including those patients who died in the hospital, yielded similar findings in attributable LOS due to severe AH. In addition, when we added patients with only 1 SBP documented in the ED to the study cohort, the severe AH prevalence changed negligibly from 13.8% to 13.0%, and the associated mortality remained unchanged at 3.4%. Models using the mix model approach, which take into account hospital‐specific effects, showed similar results.
DISCUSSION
This large‐scale analysis demonstrated that severe AH was present in 13.8% of inpatients admitted through the ED. The prevalence of severe AH varied based on the primary reason for an acute care admission, ranging from 7% in infectious syndromes to nearly 30% in acute neurologic processes. Specific to patients with neurologic disease, initial severe blood pressure elevations independently correlated with mortality. Severe blood pressure elevations at ED were independently associated with an increased need for MV on admission and a prolonged LOS across a range of disease states.
Prior work on hypertension at admission has generally included single‐center analyses or only focused on patients with specific admitting diagnoses. For example, in a single ED analysis, Tilman et al.17 reported that 16% of 10,000 patients presented with elevated blood pressure (140/90 mmHg). In a multicenter review of 7000 persons, Karras et al.18, 19 described 423 patients with severe AH (180/110 mmHg) who comprised 6% of patients seen in the ED during a 1‐week period. Qureshi et al.5 noted severe AH in 13% of patients with acute stroke. While other ED‐based studies examined all ED patients, including those admitted and discharged from the ED, our study focused on those requiring hospitalization. This disparity in illness severity may, in part, explain the higher prevalence of severe AH we noted relative to others.
We further found that the prevalence of severe AH varied based on admitting diagnosis. This difference in prevalence rates by condition seems clinically plausible. Recognition of this pattern may prove valuable to hospitalists who, by the nature of their responsibilities, will encounter a broad range of patients. Because our data were derived from the largest analysis of blood pressure assessments for ED patients who were eventually hospitalized, and encompassed a multiplicity of hospitals, our findings are likely generalizable. Moreover, our large sample size enabled us to examine severe AH at each 10 mmHg increment across a variety of disease states, rather than restricting our analysis to 1 admitting diagnosis.
The independent relationship between severe blood pressure elevation and mortality was detected only in those with neurologic conditions. Incremental increases in SBP beyond 180 mmHg were associated with a stepwise escalation in the risk for death. Although recognition of the importance of blood pressure management in both ischemic and hemorrhagic stroke remains a cornerstone of therapy for these diseases, the stepwise relationship between escalating blood pressure and outcome suggests that further study is needed to determine the optimal management of severe AH among these patients. This relationship, along with the independent association between severe AH at presentation and the need for MV, underscores the importance of severe AH in critical care, representing a major challenge for intensivists and hospitalists, particularly those who practice in neurologic ICUs.
The independent association of severe AH and prolonged LOS represents a novel finding. Few reports have correlated the initial blood pressure with measures of resource use. Katz et al.4 found a median LOS of 6 days among 1000 patients who presented with severe AH and end organ dysfunction in 25 US hospitals. These investigators, however, did not explore the incremental independent contribution of initial blood pressure to LOS. Biologically, severe hypertension may exacerbate both acute and chronic conditions, thus complicating their management and resulting in longer hospitalizations.
Our analysis has limitations. While exposure misclassification is a potential concern, unlike other population‐based studies that use ICD‐9‐CM codes to identify AH cases, we relied on actual measures of blood pressure to identify subjects, thus minimizing this threat to validity. Similarly, since our end pointsmortality, MV on admission, and LOSwere also objective measures, the probability of their misclassification is minimal.
Another concerning contributor to exposure misclassification is the possibility that, in some instances, the initial elevation in BP meeting the inclusion criteria in our study cohort does not reflect the true BP. Indeed, a substantial body of research about BP measurement in the ED suggests that we may have included some persons who likely did not have AH. For example, Pitts and Adams described a regression to the mean phenomenon, with serial BP measurements in the ED, wherein the BP fell by approximately 11 mmHg over 4 hours.20 Baumann and colleagues reported a similar pattern.21 However, the findings of these 2 analyses do not necessarily apply to our study population; we focused on patients admitted to the hospital with an acute condition, while Pitts and Adams20 and Baumann et al.21 examined all ED patients. This distinction is crucial in that patients not admitted are likely less severely ill and systematically different from those who do merit hospitalization. Moreover, both Pitts and Adams20 and Cienki et al.22 observed that the regression to the mean and fall in serial BP measurements were less pronounced in those with the most extensive BP elevations. In our study, we found the strongest relationship between adverse outcomes and BP in patients with the most extreme BP elevations. Thus, misclassification is perhaps less likely to be an issue for these subjects.
In addition, misclassification may result when initially elevated BP simply represents the impact of untreated pain or anxiety in ED patients. However, Backer et al.23 and Tanabe and colleagues24 specifically explored the impact of pain and anxiety on BP in ED subjects, and neither group found a correlation between BP and either acute pain and/or anxiety scores. Our difficulties with case definitions and BP measurements for severe AH demonstrate the need for the creation and adoption of a formal, systematic approach to this syndrome, along with the need for prospective analyses to confirm our findings.
Selection bias represents a second potential threat to validity in our observational study, although this bias is mitigated by including all consecutive acute inpatient admissions to the participating hospitals. Furthermore, inclusion of the 9% of patients who had only 1 ED measurement of SBP collected did not alter the estimate of severe AH prevalence or associated outcomes.
Third, confounding may introduce the potential for false associations derived from observational data. The large sample size of our cohort allowed us to address this concern by adjusting for a large array of confounders. In addition, unlike other large‐scale population‐based studies which typically rely on administrative ICD‐9‐CM codes for risk adjustment, our analysis incorporated actual physiologic and laboratory results measured on admission, as well as a validated severity‐of‐illness scoring system for risk adjustment.12, 13
Although both SBP and diastolic blood pressure (DBP) thresholds are included in traditional definitions of hypertension, selecting SBP as the primary measure is reasonable because SBP >180 mmHg is a more important risk factor for cardiovascular disease than elevated DBP.25 Previous studies reported the relationship between the trend of SBP over time and clinical outcomes,26, 27 but we were not able to investigate the relationship of SBP trend and outcomes because the BP measurements in the our study were not collected in predefined intervals.
It would be ideal if serial blood pressure measures were to be collected at pre‐specified intervals and if more sophisticated schemas were to be used to refine the AH definition. This type of study may be possible in the future when vital signs can be collected automatically with advanced technology. Likewise, electronically captured treatment data could further help researchers to study the impact of process‐of‐care variables, including medications and other management strategies, in relation to outcomes. Finally, outpatient management of chronic hypertension is an integral part of clinical management. Unfortunately, these types of data are not available in our existing database. These limitations notwithstanding, an in‐depth understanding of the association between severe AH and potential adverse clinical and economic outcomes may direct further research in this field.
CONCLUSION
Severe AH appears common and its prevalence varies by underlying clinical condition in patients admitted from the ED. In those with acute neurologic syndromes, the degree of blood pressure elevation correlated with mortality, need for MV, and longer LOS. For many other conditions, elevation of blood pressure appeared to be linked to an increased need for MV and a prolongation in LOS. Future studies are needed to examine the potential impact of both 1) improved long‐term outpatient BP management, and 2) optimal management of severe AH upon admission on improving outcomes of patients hospitalized from the ED with severe AH.
Chronic hypertension affects 32% of adults in the United States.1 Each year, over 20 million emergency department visits involve hypertension.2 While many studies describe the epidemiology and outcomes of chronic hypertension, less is known about severe acute hypertension (AH). Often classified as either hypertensive urgency or emergency, it receives little attention in national treatment guidelines.3 There is a limited understanding of the epidemiology of, and the relationship between, this syndrome and patient outcomes among hospitalized patients. One registry study suggested that severe AH was associated with high rates of new organ damage, need for treatment in the intensive care unit, and a 90‐day readmission rate of 10%.4 This investigation, however, lacked generalizability, because it only enrolled subjects requiring therapy with an intravenous antihypertensive agent and did not provide information on the prevalence of severe AH. Qureshi et al.5 analyzed information in a more representative sample from the national hospital ambulatory care survey, however, the only outcome examined was the correlation between acute blood pressure and stroke. Studies focusing on a selected patient population may be of limited value to hospitalists, because they are commonly required to treat a range of patients presenting to the hospital.
In light of severe syndromes that may be associated with, or complicated by, severe AH presented early during acute care, hospitalists require a better understanding of the prevalence and the relationship between severe AH and attendant mortality and morbidity. In addition, an assessment of the association of severe AH on the need for intensive care unit (ICU) admission and mechanical ventilation (MV) may aid the initial treatment assessments and triage decisions required of hospitalists.
Our objective was to describe the prevalence and implications of severe AH present at the time of evaluation in the ED among patients eventually hospitalized, using clinical data collected on all consecutive admissions across a range of clinical conditions. We sought to determine the independent contribution, if any, of severe AH to hospital mortality, need for MV on admission, as well as hospital length of stay (LOS).
METHODS
Study Design and Setting
This was a retrospective analysis of adults admitted to 114 acute‐care hospitals in the United States from 2005 through 2007. The New England Institutional Review Board/Human Subjects Research Committee (Wellesley, MA) reviewed and approved this study. It was conducted in compliance with the Health Insurance Portability and Accountability Act (HIPAA).
Data
Data were obtained from one of the Clinical Research Databases from CareFusion (formerly Cardinal Health [CareFusion Clinical Research Services, Marlborough, MA]).613 The database contains electronically imported or manually extracted demographic, clinical (eg, comorbidities, vital signs, laboratory values, other clinical findings), and administrative data (eg, diagnosis, procedures, and length of hospitalization). All vital signs were manually extracted, including the highest and the lowest ED systolic blood pressure (SBP) measurements during the ED stay, but before inpatient admission. Patients admitted for childbirth or mental health reasons were not included.
Patients
Our main analysis focused on patients whose highest and lowest SBP were collected in the ED. These patients accounted for approximately 90% of all inpatients admitted through the ED. For the approximately 9% of patients who had only 1 SBP collected, we conducted a sensitivity analysis by both including and excluding them in the main analysis to determine if the absence of a second SBP measurement altered our findings. Patients were grouped into 1 of the following 8 mutually exclusive strata based on the maximum SBP (mmHg) in the ED: <100, 101‐139, 140‐180, 181‐190, 191‐200, 201‐210, 211‐220, and >220.
Measures
The primary exposure of interest was the prevalence of severe AH, defined as at least 1 SBP measurement recorded in the ED in excess of 180 mmHg. Outcome measures included in‐hospital mortality, need for MV on admission day (defined by International Classification of Diseases, 9th revision, Clinical Modification [ICD‐9‐CM] procedure codes of 96.70, 96.71, and 96.72), and LOS. We stratified these outcomes for each inpatient admission according to 1 of 112 mutually exclusive groups of principal diagnoses (see Supporting Appendix A in the online version of this article). To simplify the presentations, we pooled the groups into 9 major disease categories based on organ systems.
Primary Data Analysis
All statistical analyses were performed using Statistical Analysis Software (SAS version 9.01; SAS Institute Inc, Cary, NC). For evaluating trending, we used the Cochran‐Armitage test for dichotomous variables (mortality and MV), and linear regression for continuous variable (LOS). We employed a logistic regression model to estimate risk of mortality and need for MV on admission. We used linear regression models to estimate the LOS associated with severe AH. We modeled each outcome as a function of patient disease severity and SBP strata. Because patients with the most severe AH tended to have higher mortality early in hospitalization, our analysis of LOS was limited to patients who survived index hospitalization.
The original disease‐specific risk‐adjustment models accounting for patient‐level confounding risk factors, including demographics, physiologic presentation on admission (vital signs, altered mental status, and laboratory findings), and chronic conditions, were previously developed and validated.12, 13 We recalibrated each of the 112 models, for the current study cohort, using the logit of predicted probability of death generated from the mortality risk‐adjustment model as a propensity score of disease severity. Using this propensity score as an aggregate severity adjuster, we refit 9 logistic regression models (1 for each major disease category) to estimate the odds ratios for mortality or need for MV for each of the 7 SBP strata in the regression models with 101‐139 mmHg as the reference group. To estimate the attributable LOS (if any) of severe AH among survivors, we fit the 9 LOS models using log transformed LOS (to normalize the potentially skewed distribution of LOS) as the outcome, controlling for disease severity. The attributable LOS and 95% confidence intervals (CI) were estimated from 1000 bootstrap iterations, with the median as the parameter estimate and 2.5th and 97.5th percentile as 95% CI.14, 15
Sensitivity Analysis
To address potential bias of LOS associated with inpatient mortality, we refit 9 LOS models, including both patients who died and those who survived the index hospitalization. The models adjusted for disease severity, mortality, and the interaction of severity and mortality. Because patients with only 1 SBP recorded at ED may be different from those with more than 1 SBP recorded, we conducted analysis by adding these patients in the study cohort to examine the potential change of overall prevalence of AH and associated mortality for the study population.
To address the potential for a center‐specific effect on outcomes, we refit all the models using a mixed model approach.16 The mixed model accounts for both patient‐level risk factors and hospital‐specific effects on the observed outcomes.
RESULTS
Patient Characteristics
The study cohort was comprised of 1,290,804 adults who were admitted through the ED, from 2005 through 2007, and whose highest and lowest SBP measurements were collected in the ED. Median age was 69 years (interquartile range, 53‐81) for the overall population. Median age was 74 (interquartile range, 60‐83) for patients with severe AH (Table 1). Hospital mortality was 3.6% (n = 46,033), with 6119 (13.3%) having severe AH.
| Characteristic | Prevalence, n (Column %) | Severe Acute Hypertension, n (Row %) |
|---|---|---|
| Total number of discharges | 1,290,804 (100.0) | 178,197 (13.8) |
| Mortality | 46,033 (3.6) | 6,119 (13.3) |
| Live discharges | 1,244,771 (96.4) | 172,078 (13.8) |
| Mechanical ventilation on admission | 39,238 (3.0) | 9,508 (24.2) |
| Demographics | ||
| Age, median (1st, 3rd quartiles) | 69 (53, 81) | 74 (60, 83) |
| Male | 587,553 (45.5) | 71,085 (12.1) |
| Female | 703,244 (54.5) | 107,109 (15.2) |
| Race | ||
| White | 949,869 (73.6) | 121,930 (12.8) |
| Black | 220,601 (17.1) | 39,667 (18.0) |
| Other | 120,334 (9.3) | 16,600 (13.8) |
| Insurance | ||
| Medicare | 668,420 (51.8) | 105,078 (15.7) |
| Medicaid | 108,538 (8.4) | 12,259 (11.3) |
| Commercial | 163,858 (12.7) | 18,669 (11.4) |
| Other | 349,988 (27.1) | 42,191 (12.1) |
| Disease system by the principal diagnosis | ||
| Nervous system | 76,744 (5.9) | 22,270 (29.0) |
| Respiratory system | 222,329 (17.2) | 24,678 (11.1) |
| Circulatory system | 416,847 (32.3) | 66,852 (16.0) |
| Digestive system | 186,282 (14.4) | 17,817 (9.6) |
| Hepatobiliary/pancreas system | 52,293 (4.1) | 5,664 (10.8) |
| Endocrine system | 45,050 (3.5) | 6,625 (14.7) |
| Kidney/urinary system | 81,782 (6.3) | 11,050 (13.5) |
| Infectious diseases | 60,353 (4.7) | 4,162 (6.9) |
| Other | 149,124 (11.6) | 19,079 (12.8) |
| Comorbidity by secondary diagnoses | ||
| Hypertension | 729,417 (56.5) | 135,498 (18.6) |
| Fluid and electrolyte disorders | 306,666 (23.8) | 37,836 (12.3) |
| Diabetes without chronic complications | 286,912 (22.2) | 47,979 (16.7) |
| Chronic pulmonary disease | 283,895 (22.0) | 35,977 (12.7) |
| Congestive heart failure | 213,523 (16.5) | 33,956 (15.9) |
| Deficiency anemias | 210,230 (16.3) | 30,266 (14.4) |
| Renal failure | 159,409 (12.3) | 31,984 (20.1) |
| Hypothyroidism | 153,911 (11.9) | 22,441 (14.6) |
| Valvular disease | 140,820 (10.9) | 21,453 (15.2) |
| Depression | 137,259 (10.6) | 16,886 (12.3) |
| Other neurological disorders | 126,954 (9.8) | 19,103 (15.0) |
| Peripheral vascular disease | 88,321 (6.8) | 16,180 (18.3) |
| Obesity | 84,000 (6.5) | 12,351 (14.7) |
| Diabetes with chronic complications | 65,989 (5.1) | 13,093 (19.8) |
| Psychoses | 54,769 (4.2) | 5,555 (10.1) |
| Alcohol abuse | 51,765 (4.0) | 6,014 (11.6) |
| Pulmonary circulation disease | 49,248 (3.8) | 7,128 (14.5) |
| Coagulopathy | 43,584 (3.4) | 4,339 (10.0) |
| Paralysis | 42,128 (3.3) | 8,125 (19.3) |
| Drug abuse | 36,134 (2.8) | 4,779 (13.2) |
| Liver disease | 36,094 (2.8) | 3,218 (8.9) |
| Weight loss | 35,795 (2.8) | 3,726 (10.4) |
| Metastatic cancer | 33,517 (2.6) | 2,498 (7.5) |
| Rheumatoid arthritis | 32,545 (2.5) | 4,300 (13.2) |
| Solid tumor without metastasis | 30,677 (2.4) | 3,035 (9.9) |
| Chronic blood loss anemia | 25,416 (2.0) | 2,268 (8.9) |
| Lymphoma | 9,972 (0.8) | 871 (8.7) |
| Acquired immune deficiency syndrome | 3,048 (0.2) | 307 (10.1) |
| Peptic ulcer disease | 915 (0.1) | 131 (14.3) |
| Discharges by hospital characteristics | ||
| Teaching status | ||
| Teaching hospitals | 899,786 (69.7) | 127,512 (14.2) |
| Nonteaching hospitals | 391,018 (30.3) | 50,685 (13.0) |
| Urban status | ||
| Urban hospitals | 1,164,802 (90.2) | 162,399 (13.9) |
| Rural hospitals | 126,002 (9.8) | 15,798 (12.5) |
| Bed size | ||
| Beds <100 | 36,624 (2.8) | 4,965 (13.6) |
| Beds 100‐300 | 623,327 (48.3) | 80,156 (12.9) |
| Beds >300 | 630,853 (48.9) | 93,076 (14.8) |
Prevalence of Acute Hypertension
A total of 763,634 (59.2%) patients had at least 1 SBP measurement of 140 mmHg during the ED stay, including 178,197 (13.8%) with SBP >180 mmHg. Body systems associated with the highest prevalence of severe AH (SBP >180 mmHg) were nervous (29.0%), circulatory (16.0%), endocrine (14.7%), and kidney/urinary (13.5%) (Figure 1 presents the data in graphic form; Supporting Appendix B, in the online version of this article, presents corresponding data in table form).
Mortality
Univariable analysis revealed a graded relationship between SBP stratum and mortality risk (Figure 2a; and see Supporting Appendix C in the online version of this article). This relationship was most pronounced for nervous system diseases; mortality rates for each 10 mmHg increase in SBP from 180 to >220 mmHg were 6.5%, 8.1%, 10.0%, 12.0%, and 19.7%, respectively (trending P < 0.0001). The risk‐adjusted increase in mortality odds ratio ranged from 1.04 (95% CI: 0.89, 1.21) to 1.44 (95% CI: 1.25, 1.67) for patients in the severe AH strata compared to patients with SBP of 101 to 139 mmHg (Figure 3). Severe AH was not an independent mortality predictor in other disease categories.
Mechanical Ventilation on Admission
Univariable analysis revealed a graded relationship between severe AH and a need for MV on admission, especially for respiratory, circulatory, and infectious conditions (trending P < 0.0001) (Figure 2b; and see Supporting Appendix C in the online version of this article). In the multivariable analysis, there was a relationship between severe AH stratum and adjusted risk for MV on admission across nearly all disease categories (Figure 4).
Length of Stay
Univariable analysis revealed a graded relationship between severe AH strata and LOS for nearly all disease categories in survivors (trending P < 0.0001), except for digestive, kidney, and infectious diseases (Figure 2c; and see Supporting Appendix C in the online version of this article). For patients with nervous system diseases, the unadjusted LOS for each 10 mmHg increase in SBP from 180 to >220 was 5.8, 6.1, 6.4, 6.8, and 8.0 days, respectively (trending P < 0.0001). The relationship was similar for other disease categories which showed significant trending.
In the multivariable analysis, the relationship between severe AH strata and adjusted attributable LOS was graded across most disease categories, especially nervous, circulatory, and hepatobiliary diseases (Figure 5). The total adjusted number of hospital days attributable to severe AH was 0.43 days per case for all survivors with severe AH.
Sensitivity Analysis
Our sensitivity analysis of the LOS estimate, including those patients who died in the hospital, yielded similar findings in attributable LOS due to severe AH. In addition, when we added patients with only 1 SBP documented in the ED to the study cohort, the severe AH prevalence changed negligibly from 13.8% to 13.0%, and the associated mortality remained unchanged at 3.4%. Models using the mix model approach, which take into account hospital‐specific effects, showed similar results.
DISCUSSION
This large‐scale analysis demonstrated that severe AH was present in 13.8% of inpatients admitted through the ED. The prevalence of severe AH varied based on the primary reason for an acute care admission, ranging from 7% in infectious syndromes to nearly 30% in acute neurologic processes. Specific to patients with neurologic disease, initial severe blood pressure elevations independently correlated with mortality. Severe blood pressure elevations at ED were independently associated with an increased need for MV on admission and a prolonged LOS across a range of disease states.
Prior work on hypertension at admission has generally included single‐center analyses or only focused on patients with specific admitting diagnoses. For example, in a single ED analysis, Tilman et al.17 reported that 16% of 10,000 patients presented with elevated blood pressure (140/90 mmHg). In a multicenter review of 7000 persons, Karras et al.18, 19 described 423 patients with severe AH (180/110 mmHg) who comprised 6% of patients seen in the ED during a 1‐week period. Qureshi et al.5 noted severe AH in 13% of patients with acute stroke. While other ED‐based studies examined all ED patients, including those admitted and discharged from the ED, our study focused on those requiring hospitalization. This disparity in illness severity may, in part, explain the higher prevalence of severe AH we noted relative to others.
We further found that the prevalence of severe AH varied based on admitting diagnosis. This difference in prevalence rates by condition seems clinically plausible. Recognition of this pattern may prove valuable to hospitalists who, by the nature of their responsibilities, will encounter a broad range of patients. Because our data were derived from the largest analysis of blood pressure assessments for ED patients who were eventually hospitalized, and encompassed a multiplicity of hospitals, our findings are likely generalizable. Moreover, our large sample size enabled us to examine severe AH at each 10 mmHg increment across a variety of disease states, rather than restricting our analysis to 1 admitting diagnosis.
The independent relationship between severe blood pressure elevation and mortality was detected only in those with neurologic conditions. Incremental increases in SBP beyond 180 mmHg were associated with a stepwise escalation in the risk for death. Although recognition of the importance of blood pressure management in both ischemic and hemorrhagic stroke remains a cornerstone of therapy for these diseases, the stepwise relationship between escalating blood pressure and outcome suggests that further study is needed to determine the optimal management of severe AH among these patients. This relationship, along with the independent association between severe AH at presentation and the need for MV, underscores the importance of severe AH in critical care, representing a major challenge for intensivists and hospitalists, particularly those who practice in neurologic ICUs.
The independent association of severe AH and prolonged LOS represents a novel finding. Few reports have correlated the initial blood pressure with measures of resource use. Katz et al.4 found a median LOS of 6 days among 1000 patients who presented with severe AH and end organ dysfunction in 25 US hospitals. These investigators, however, did not explore the incremental independent contribution of initial blood pressure to LOS. Biologically, severe hypertension may exacerbate both acute and chronic conditions, thus complicating their management and resulting in longer hospitalizations.
Our analysis has limitations. While exposure misclassification is a potential concern, unlike other population‐based studies that use ICD‐9‐CM codes to identify AH cases, we relied on actual measures of blood pressure to identify subjects, thus minimizing this threat to validity. Similarly, since our end pointsmortality, MV on admission, and LOSwere also objective measures, the probability of their misclassification is minimal.
Another concerning contributor to exposure misclassification is the possibility that, in some instances, the initial elevation in BP meeting the inclusion criteria in our study cohort does not reflect the true BP. Indeed, a substantial body of research about BP measurement in the ED suggests that we may have included some persons who likely did not have AH. For example, Pitts and Adams described a regression to the mean phenomenon, with serial BP measurements in the ED, wherein the BP fell by approximately 11 mmHg over 4 hours.20 Baumann and colleagues reported a similar pattern.21 However, the findings of these 2 analyses do not necessarily apply to our study population; we focused on patients admitted to the hospital with an acute condition, while Pitts and Adams20 and Baumann et al.21 examined all ED patients. This distinction is crucial in that patients not admitted are likely less severely ill and systematically different from those who do merit hospitalization. Moreover, both Pitts and Adams20 and Cienki et al.22 observed that the regression to the mean and fall in serial BP measurements were less pronounced in those with the most extensive BP elevations. In our study, we found the strongest relationship between adverse outcomes and BP in patients with the most extreme BP elevations. Thus, misclassification is perhaps less likely to be an issue for these subjects.
In addition, misclassification may result when initially elevated BP simply represents the impact of untreated pain or anxiety in ED patients. However, Backer et al.23 and Tanabe and colleagues24 specifically explored the impact of pain and anxiety on BP in ED subjects, and neither group found a correlation between BP and either acute pain and/or anxiety scores. Our difficulties with case definitions and BP measurements for severe AH demonstrate the need for the creation and adoption of a formal, systematic approach to this syndrome, along with the need for prospective analyses to confirm our findings.
Selection bias represents a second potential threat to validity in our observational study, although this bias is mitigated by including all consecutive acute inpatient admissions to the participating hospitals. Furthermore, inclusion of the 9% of patients who had only 1 ED measurement of SBP collected did not alter the estimate of severe AH prevalence or associated outcomes.
Third, confounding may introduce the potential for false associations derived from observational data. The large sample size of our cohort allowed us to address this concern by adjusting for a large array of confounders. In addition, unlike other large‐scale population‐based studies which typically rely on administrative ICD‐9‐CM codes for risk adjustment, our analysis incorporated actual physiologic and laboratory results measured on admission, as well as a validated severity‐of‐illness scoring system for risk adjustment.12, 13
Although both SBP and diastolic blood pressure (DBP) thresholds are included in traditional definitions of hypertension, selecting SBP as the primary measure is reasonable because SBP >180 mmHg is a more important risk factor for cardiovascular disease than elevated DBP.25 Previous studies reported the relationship between the trend of SBP over time and clinical outcomes,26, 27 but we were not able to investigate the relationship of SBP trend and outcomes because the BP measurements in the our study were not collected in predefined intervals.
It would be ideal if serial blood pressure measures were to be collected at pre‐specified intervals and if more sophisticated schemas were to be used to refine the AH definition. This type of study may be possible in the future when vital signs can be collected automatically with advanced technology. Likewise, electronically captured treatment data could further help researchers to study the impact of process‐of‐care variables, including medications and other management strategies, in relation to outcomes. Finally, outpatient management of chronic hypertension is an integral part of clinical management. Unfortunately, these types of data are not available in our existing database. These limitations notwithstanding, an in‐depth understanding of the association between severe AH and potential adverse clinical and economic outcomes may direct further research in this field.
CONCLUSION
Severe AH appears common and its prevalence varies by underlying clinical condition in patients admitted from the ED. In those with acute neurologic syndromes, the degree of blood pressure elevation correlated with mortality, need for MV, and longer LOS. For many other conditions, elevation of blood pressure appeared to be linked to an increased need for MV and a prolongation in LOS. Future studies are needed to examine the potential impact of both 1) improved long‐term outpatient BP management, and 2) optimal management of severe AH upon admission on improving outcomes of patients hospitalized from the ED with severe AH.
- Health, United States, 2010.Hyattsville, MD:Centers for Disease Control and Prevention, National Center for Health Statistics;2011.
- Agency for Healthcare Research and Quality.Healthcare Cost and Utilization Project (HCUP).Rockville, MD:US Department of Health 289(19):2560–2572.
- ,,, et al.Practice patterns, outcomes, and end‐organ dysfunction for patients with acute severe hypertension: the Studying the Treatment of Acute hyperTension (STAT) registry.Am Heart J.2009;158(4):599–606.e1.
- ,,, et al.Prevalence of elevated blood pressure in 563,704 adult patients with stroke presenting to the ED in the United States.Am J Emerg Med.2007;25(1):32–38.
- ,,, et al.A prediction rule to identify low‐risk patients with community‐acquired pneumonia.N Engl J Med.1997;336(4):243–250.
- ,.A clinical assessment of MedisGroups.JAMA.1988;260(21):3159–3163.
- ,,,,,.Healthcare‐associated bloodstream infection: a distinct entity? Insights from a large U.S. database.Crit Care Med.2006;34(10):2588–2595.
- ,,,,,.Epidemiology and outcomes of health‐care‐associated pneumonia: results from a large US database of culture‐positive pneumonia.Chest.2005;128(6):3854–3862.
- ,,,,,.Morbidity and cost burden of methicillin‐resistant Staphylococcus aureus in early onset ventilator‐associated pneumonia.Crit Care.2006;10(3):R97.
- ,,,,.Evaluation of the complication rate as a measure of quality of care in coronary artery bypass graft surgery.JAMA.1995;274(4):317–323.
- ,,.Using automated clinical data for risk adjustment: development and validation of six disease‐specific mortality predictive models for pay‐for‐performance.Med Care.2007;45(8):789–805.
- ,,,,.Development and validation of a disease‐specific risk adjustment system using automated clinical data.Health Service Research.2010;45:1815–1835.
- ,.Estimating marginal and incremental effects on health outcomes using flexible link and variance function models.Biostatistics.2005;6(1):93–109.
- ,.An Introduction to the Bootstrap.London, England:Chapman 1993.
- ,.Applied Mixed Models in Medicine.2nd ed.Chichester, England:Wiley;2006.
- ,,,,,.Recognizing asymptomatic elevated blood pressure in ED patients: how good (bad) are we?Am J Emerg Med.2007;25(3):313–317.
- ,,, et al.Evaluation and treatment of patients with severely elevated blood pressure in academic emergency departments: a multicenter study.Ann Emerg Med.2006;47(3):230–236.
- ,,, et al.Elevated blood pressure in urban emergency department patients.Acad Emerg Med.2005;12(9):835–843.
- ,.Emergency department hypertension and regression to the mean.Ann Emerg Med.1998;31(2):214–218.
- ,,,.Differing prevalence estimates of elevated blood pressure in ED patients using 4 methods of categorization.Am J Emerg Med.2008;26(5):561–565.
- ,,.The validity of emergency department triage blood pressure measurements.Acad Emerg Med.2004;11(3):237–243.
- ,,.Reproducibility of increased blood pressure during an emergency department or urgent care visit.Ann Emerg Med.2003;41(4):507–512.
- ,,,,,.Increased blood pressure in the emergency department: pain, anxiety, or undiagnosed hypertension?Ann Emerg Med.2008;51(3):221–229.
- ,,.Clinical Advisory Statement. Importance of systolic blood pressure in older Americans.Hypertension.2000;35(5):1021–1024.
- ,,,.High blood pressure in early acute stroke: a sign of a poor outcome?J Hypertens.2006;24(2):381–386.
- ,,,,.Blood pressure as an independent prognostic factor in acute ischemic stroke.Can J Neurol Sci.2006;33(1):34–38.
- Health, United States, 2010.Hyattsville, MD:Centers for Disease Control and Prevention, National Center for Health Statistics;2011.
- Agency for Healthcare Research and Quality.Healthcare Cost and Utilization Project (HCUP).Rockville, MD:US Department of Health 289(19):2560–2572.
- ,,, et al.Practice patterns, outcomes, and end‐organ dysfunction for patients with acute severe hypertension: the Studying the Treatment of Acute hyperTension (STAT) registry.Am Heart J.2009;158(4):599–606.e1.
- ,,, et al.Prevalence of elevated blood pressure in 563,704 adult patients with stroke presenting to the ED in the United States.Am J Emerg Med.2007;25(1):32–38.
- ,,, et al.A prediction rule to identify low‐risk patients with community‐acquired pneumonia.N Engl J Med.1997;336(4):243–250.
- ,.A clinical assessment of MedisGroups.JAMA.1988;260(21):3159–3163.
- ,,,,,.Healthcare‐associated bloodstream infection: a distinct entity? Insights from a large U.S. database.Crit Care Med.2006;34(10):2588–2595.
- ,,,,,.Epidemiology and outcomes of health‐care‐associated pneumonia: results from a large US database of culture‐positive pneumonia.Chest.2005;128(6):3854–3862.
- ,,,,,.Morbidity and cost burden of methicillin‐resistant Staphylococcus aureus in early onset ventilator‐associated pneumonia.Crit Care.2006;10(3):R97.
- ,,,,.Evaluation of the complication rate as a measure of quality of care in coronary artery bypass graft surgery.JAMA.1995;274(4):317–323.
- ,,.Using automated clinical data for risk adjustment: development and validation of six disease‐specific mortality predictive models for pay‐for‐performance.Med Care.2007;45(8):789–805.
- ,,,,.Development and validation of a disease‐specific risk adjustment system using automated clinical data.Health Service Research.2010;45:1815–1835.
- ,.Estimating marginal and incremental effects on health outcomes using flexible link and variance function models.Biostatistics.2005;6(1):93–109.
- ,.An Introduction to the Bootstrap.London, England:Chapman 1993.
- ,.Applied Mixed Models in Medicine.2nd ed.Chichester, England:Wiley;2006.
- ,,,,,.Recognizing asymptomatic elevated blood pressure in ED patients: how good (bad) are we?Am J Emerg Med.2007;25(3):313–317.
- ,,, et al.Evaluation and treatment of patients with severely elevated blood pressure in academic emergency departments: a multicenter study.Ann Emerg Med.2006;47(3):230–236.
- ,,, et al.Elevated blood pressure in urban emergency department patients.Acad Emerg Med.2005;12(9):835–843.
- ,.Emergency department hypertension and regression to the mean.Ann Emerg Med.1998;31(2):214–218.
- ,,,.Differing prevalence estimates of elevated blood pressure in ED patients using 4 methods of categorization.Am J Emerg Med.2008;26(5):561–565.
- ,,.The validity of emergency department triage blood pressure measurements.Acad Emerg Med.2004;11(3):237–243.
- ,,.Reproducibility of increased blood pressure during an emergency department or urgent care visit.Ann Emerg Med.2003;41(4):507–512.
- ,,,,,.Increased blood pressure in the emergency department: pain, anxiety, or undiagnosed hypertension?Ann Emerg Med.2008;51(3):221–229.
- ,,.Clinical Advisory Statement. Importance of systolic blood pressure in older Americans.Hypertension.2000;35(5):1021–1024.
- ,,,.High blood pressure in early acute stroke: a sign of a poor outcome?J Hypertens.2006;24(2):381–386.
- ,,,,.Blood pressure as an independent prognostic factor in acute ischemic stroke.Can J Neurol Sci.2006;33(1):34–38.
Copyright © 2011 Society of Hospital Medicine
Inappropriate Treatment of HCA‐cSSTI
Classically, infections have been categorized as either community‐acquired (CAI) or nosocomial in origin. Until recently, this scheme was thought adequate to capture the differences in the microbiology and outcomes in the corresponding scenarios. However, recent evidence suggests that this distinction may no longer be valid. For example, with the spread and diffusion of healthcare delivery beyond the confines of the hospital along with the increasing use of broad spectrum antibiotics both in and out of the hospital, pathogens such as methicillin‐resistant Staphylococcus aureus (MRSA) and Pseudomonas aeruginosa (PA), traditionally thought to be confined to the hospital, are now seen in patients presenting from the community to the emergency department (ED).1, 2 Reflecting this shift in epidemiology, some national guidelines now recognize healthcare‐associated infection (HCAI) as a distinct entity.3 The concept of HCAI allows the clinician to identify patients who, despite suffering a community onset infection, still may be at risk for a resistant bacterial pathogen. Recent studies in both bloodstream infection and pneumonia have clearly demonstrated that those with HCAI have distinct microbiology and outcomes relative to those with pure CAI.47
Most work focusing on establishing HCAI has not addressed skin and soft tissue infections. These infections, although not often fatal, account for an increasing number of admissions to the hospital.8, 9 In addition, they may be associated with substantial morbidity and cost.8 Given that many pathogens such as S. aureus, which may be resistant to typical antimicrobials used in the ED, are also major culprits in complicated skin and skin structure infections (cSSSI), the HCAI paradigm may apply in cSSSI. Furthermore, because of these patterns of increased resistance, HCA‐cSSSI patients, similar to other HCAI groups, may be at an increased risk of being treated with initially inappropriate antibiotic therapy.7, 10
Since in the setting of other types of infection inappropriate empiric treatment has been shown to be associated with increased mortality and costs,7, 1015 and since indirect evidence suggests a similar impact on healthcare utilization among cSSSI patients,8 we hypothesized that among a cohort of patients hospitalized with a cSSSI, the initial empiric choice of therapy is independently associated with hospital length of stay (LOS). We performed a retrospective cohort study to address this question.
Methods
Study Design
We performed a single‐center retrospective cohort study of patients with cSSSI admitted to the hospital through the ED. All consecutive patients hospitalized between April 2006 and December 2007 meeting predefined inclusion criteria (see below) were enrolled. The study was approved by the Washington University School of Medicine Human Studies Committee, and informed consent was waived. We have previously reported on the characteristics and outcomes of this cohort, including both community‐acquired and HCA‐cSSSI patients.16
Study Cohort
All consecutive patients admitted from the community through the ED between April 2006 and December 2007 at the Barnes‐Jewish Hospital, a 1200‐bed university‐affiliated, urban teaching hospital in St. Louis, MO were included if: (1) they had a diagnosis of a predefined cSSSI (see Appendix Table A1, based on reference 8) and (2) they had a positive microbiology culture obtained within 24 hours of hospital admission. Similar to the work by Edelsberg et al.8 we excluded patients if certain diagnoses and procedures were present (Appendix Table A2). Cases were also excluded if they represented a readmission for the same diagnosis within 30 days of the original hospitalization.
Definitions
HCAI was defined as any cSSSI in a patient with a history of recent hospitalization (within the previous year, consistent with the previous study16), receiving antibiotics prior to admission (previous 90 days), transferring from a nursing home, or needing chronic dialysis. We defined a polymicrobial infection as one with more than one organism, and mixed infection as an infection with both a gram‐positive and a gram‐negative organism. Inappropriate empiric therapy took place if a patient did not receive treatment within 24 hours of the time the culture was obtained with an agent exhibiting in vitro activity against the isolated pathogen(s). In mixed infections, appropriate therapy was treatment within 24 hours of culture being obtained with agent(s) active against all pathogens recovered.
Data Elements
We collected information about multiple baseline demographic and clinical factors including: age, gender, race/ethnicity, comorbidities, the presence of risk factors for HCAI, the presence of bacteremia at admission, and the location of admission (ward vs. intensive care unit [ICU]). Bacteriology data included information on specific bacterium/a recovered from culture, the site of the culture (eg, tissue, blood), susceptibility patterns, and whether the infection was monomicrobial, polymicrobial, or mixed. When blood culture was available and positive, we prioritized this over wound and other cultures and designated the corresponding organism as the culprit in the index infection. Cultures growing our coagulase‐negative S. aureus were excluded as a probable contaminant. Treatment data included information on the choice of the antimicrobial therapy and the timing of its institution relative to the timing of obtaining the culture specimen. The presence of such procedures as incision and drainage (I&D) or debridement was recorded.
Statistical Analyses
Descriptive statistics comparing HCAI patients treated appropriately to those receiving inappropriate empiric coverage based on their clinical, demographic, microbiologic and treatment characteristics were computed. Hospital LOS served as the primary and hospital mortality as the secondary outcomes, comparing patients with HCAI treated appropriately to those treated inappropriately. All continuous variables were compared using Student's t test or the Mann‐Whitney U test as appropriate. All categorical variables were compared using the chi‐square test or Fisher's exact test. To assess the attributable impact of inappropriate therapy in HCAI on the outcomes of interest, general linear models with log transformation were developed to model hospital LOS parameters; all means are presented as geometric means. All potential risk factors significant at the 0.1 level in univariate analyses were entered into the model. All calculations were performed in Stata version 9 (Statacorp, College Station, TX).
Results
Of the 717 patients with culture‐positive cSSSI admitted during the study period, 527 (73.5%) were classified as HCAI. The most common reason for classification as an HCAI was recent hospitalization. Among those with an HCA‐cSSSI, 405 (76.9%) received appropriate empiric treatment, with nearly one‐quarter receiving inappropriate initial coverage. Those receiving inappropriate antibiotic were more likely to be African American, and had a higher likelihood of having end‐stage renal disease (ESRD) than those with appropriate coverage (Table 1). While those patients treated appropriately had higher rates of both cellulitis and abscess as the presenting infection, a substantially higher proportion of those receiving inappropriate initial treatment had a decubitus ulcer (29.5% vs. 10.9%, P <0.001), a device‐associated infection (42.6% vs. 28.6%, P = 0.004), and had evidence of bacteremia (68.9% vs. 57.8%, P = 0.028) than those receiving appropriate empiric coverage (Table 2).
| Inappropriate (n = 122), n (%) | Appropriate (n = 405), n (%) | P Value | |
|---|---|---|---|
| |||
| Age, years | 56.3 18.0 | 53.6 16.7 | 0.147 |
| Gender (F) | 62 (50.8) | 190 (46.9) | 0.449 |
| Race | |||
| Caucasian | 51 (41.8) | 219 (54.1) | 0.048 |
| African American | 68 (55.7) | 178 (43.9) | |
| Other | 3 (2.5) | 8 (2.0) | |
| HCAI risk factors | |||
| Recent hospitalization* | 110 (90.2) | 373 (92.1) | 0.498 |
| Within 90 days | 98 (80.3) | 274 (67.7) | 0.007 |
| >90 and 180 days | 52 (42.6) | 170 (42.0) | 0.899 |
| >180 days and 1 year | 46 (37.7) | 164 (40.5) | 0.581 |
| Prior antibiotics | 26 (21.3) | 90 (22.2) | 0.831 |
| Nursing home resident | 29 (23.8) | 54 (13.3) | 0.006 |
| Hemodialysis | 19 (15.6) | 39 (9.7) | 0.067 |
| Comorbidities | |||
| DM | 40 (37.8) | 128 (31.6) | 0.806 |
| PVD | 5 (4.1) | 15 (3.7) | 0.841 |
| Liver disease | 6 (4.9) | 33 (8.2) | 0.232 |
| Cancer | 21 (17.2) | 85 (21.0) | 0.362 |
| HIV | 1 (0.8) | 12 (3.0) | 0.316 |
| Organ transplant | 2 (1.6) | 8 (2.0) | 1.000 |
| Autoimmune disease | 5 (4.1) | 8 (2.0) | 0.185 |
| ESRD | 22 (18.0) | 46 (11.4) | 0.054 |
| Inappropriate (n = 122), n (%) | Appropriate (n = 405), n (%) | P Value | |
|---|---|---|---|
| |||
| Cellulitis | 28 (23.0) | 171 (42.2) | <0.001 |
| Decubitus ulcer | 36 (29.5) | 44 (10.9) | <0.001 |
| Post‐op wound | 25 (20.5) | 75 (18.5) | 0.626 |
| Device‐associated infection | 52 (42.6) | 116 (28.6) | 0.004 |
| Diabetic foot ulcer | 9 (7.4) | 24 (5.9) | 0.562 |
| Abscess | 22 (18.0) | 108 (26.7) | 0.052 |
| Other* | 2 (1.6) | 17 (4.2) | 0.269 |
| Presence of bacteremia | 84 (68.9) | 234 (57.8) | 0.028 |
The pathogens recovered from the appropriately and inappropriately treated groups are listed in Figure 1. While S. aureus overall was more common among those treated appropriately, the frequency of MRSA did not differ between the groups. Both E. faecalis and E. faecium were recovered more frequently in the inappropriate group, resulting in a similar pattern among the vancomycin‐resistant enterococcal species. Likewise, P. aeruginosa, P. mirabilis, and A. baumannii were all more frequently seen in the group treated inappropriately than in the group getting appropriate empiric coverage. A mixed infection was also more likely to be present among those not exposed (16.5%) than among those exposed (7.5%) to appropriate early therapy (P = 0.001) (Figure 1).
In terms of processes of care and outcomes (Table 3), commensurate with the higher prevalence of abscess in the appropriately treated group, the rate of I&D was significantly higher in this cohort (36.8%) than in the inappropriately treated (23.0%) group (P = 0.005). Need for initial ICU care did not differ as a function of appropriateness of therapy (P = 0.635).
| Inappropriate (n = 122) | Appropriate (n = 405) | P Value | |
|---|---|---|---|
| |||
| I&D/debridement | 28 (23.0%) | 149 (36.8%) | 0.005 |
| I&D in ED | 0 | 7 (1.7) | 0.361 |
| ICU | 9 (7.4%) | 25 (6.2%) | 0.635 |
| Hospital LOS, days | |||
| Median (IQR 25, 75) [Range] | 7.0 (4.2, 13.6) [0.686.6] | 6 (3.3, 10.1) [0.748.3] | 0.026 |
| Hospital mortality | 9 (7.4%) | 26 (6.4%) | 0.710 |
The unadjusted mortality rate was low overall and did not vary based on initial treatment (Table 3). In a generalized linear model with the log‐transformed LOS as the dependent variable, adjusting for multiple potential confounders, initial inappropriate antibiotic therapy had an attributable incremental increase in the hospital LOS of 1.8 days (95% CI, 1.42.3) (Table 4).
| Factor | Attributable LOS (days) | 95% CI | P Value |
|---|---|---|---|
| |||
| Infection type: device | 3.6 | 2.74.8 | <0.001 |
| Infection type: decubitus ulcer | 3.3 | 2.64.2 | <0.001 |
| Infection type: abscess | 2.5 | 1.64.0 | <0.001 |
| Organism: P. mirabilis | 2.2 | 1.43.4 | <0.001 |
| Organism: E. faecalis | 2.1 | 1.72.6 | <0.001 |
| Nursing home resident | 2.1 | 1.62.6 | <0.001 |
| Inappropriate antibiotic | 1.8 | 1.42.3 | <0.001 |
| Race: Non‐Caucasian | 0.31 | 0.240.41 | <0.001 |
| Organism: E. faecium | 0.23 | 0.150.35 | <0.001 |
Because bacteremia is known to be an effect modifier of the relationship between the empiric choice of antibiotic and infection outcomes, we further explored its role in the HCAI cSSSI on the outcomes of interest (Table 5). Similar to the effect detected in the overall cohort, treatment with inappropriate therapy was associated with an increase in the hospital LOS, but not hospital mortality in those with bacteremia, though this phenomenon was observed only among patients with secondary bacteremia, and not among those without (Table 5).
| Bacteremia Present (n = 318) | Bacteremia Absent (n = 209) | |||||
|---|---|---|---|---|---|---|
| I (n = 84) | A (n = 234) | P Value | I (n = 38) | A (n = 171) | P Value | |
| ||||||
| Hospital LOS, days | ||||||
| Mean SD | 14.4 27.5 | 9.8 9.7 | 0.041 | 6.6 6.8 | 6.9 8.2 | 0.761 |
| Median (IQR 25, 75) | 8.8 (5.4, 13.9) | 7.0 (4.3, 11.7) | 4.4 (2.4, 7.7) | 3.9 (2.0, 8.2) | ||
| Hospital mortality | 8 (9.5%) | 24 (10.3%) | 0.848 | 1 (2.6%) | 2 (1.2%) | 0.454 |
Discussion
This retrospective analysis provides evidence that inappropriate empiric antibiotic therapy for HCA‐cSSSI independently prolongs hospital LOS. The impact of inappropriate initial treatment on LOS is independent of many important confounders. In addition, we observed that this effect, while present among patients with secondary bacteremia, is absent among those without a blood stream infection.
To the best of our knowledge, ours is the first cohort study to examine the outcomes associated with inappropriate treatment of a HCAI cSSSI within the context of available microbiology data. Edelsberg et al.8 examined clinical and economic outcomes associated with the failure of the initial treatment of cSSSI. While not specifically focusing on HCAI patients, these authors noted an overall 23% initial therapy failure rate. Among those patients who failed initial therapy, the risk of hospital death was nearly 3‐fold higher (adjusted odds ratio [OR], 2.91; 95% CI, 2.343.62), and they incurred the mean of 5.4 additional hospital days, compared to patients treated successfully with the initial regimen.8 Our study confirms Edelsberg et al.'s8 observation of prolonged hospital LOS in association with treatment failure, and builds upon it by defining the actual LOS increment attributable to inappropriate empiric therapy. It is worth noting that the study by Edelsberg et al.,8 however, lacked explicit definition of the HCAI population and microbiology data, and used treatment failure as a surrogate marker for inappropriate treatment. It is likely these differences between our two studies in the underlying population and exposure definitions that account for the differences in the mortality data between that study and ours.
It is not fundamentally surprising that early exposure to inappropriate empiric therapy alters healthcare resource utilization outcomes for the worse. Others have demonstrated that infection with a resistant organism results in prolongation of hospital LOS and costs. For example, in a large cohort of over 600 surgical hospitalizations requiring treatment for a gram‐negative infection, antibiotic resistance was an independent predictor of increased LOS and costs.15 These authors quantified the incremental burden of early gram‐negative resistance at over $11,000 in hospital costs.15 Unfortunately, the treatment differences for resistant and sensitive organisms were not examined.15 Similarly, Shorr et al. examined risk factors for prolonged hospital LOS and increased costs in a cohort of 291 patients with MRSA sterile site infection.17 Because in this study 23% of the patients received inappropriate empiric therapy, the authors were able to examine the impact of this exposure on utilization outcomes.17 In an adjusted analysis, inappropriate initial treatment was associated with an incremental increase in the LOS of 2.5 days, corresponding to the unadjusted cost differential of nearly $6,000.17 Although focusing on a different population, our results are consistent with these previous observations that antibiotic resistance and early inappropriate therapy affect hospital utilization parameters, in our case by adding nearly 2 days to the hospital LOS.
Our study has a number of limitations. First, as a retrospective cohort study it is prone to various forms of bias, most notably selection bias. To minimize the possibility of such, we established a priori case definitions and enrolled consecutive patients over a specific period of time. Second, as in any observational study, confounding is an issue. We dealt with this statistically by constructing a multivariable regression model; however, the possibility of residual confounding remains. Third, because some of the wound and ulcer cultures likely were obtained with a swab and thus represented colonization, rather than infection, we may have over‐estimated the rate of inappropriate therapy, and this needs to be followed up in future prospective studies. Similarly, we may have over‐estimated the likelihood of inappropriate therapy among polymicrobial and mixed infections as well, given that, for example, a gram‐negative organism may carry a different clinical significance when cultured from blood (infection) than when it is detected in a decubitus ulcer (potential colonization). Fourth, because we limited our cohort to patients without deep‐seated infections, such as necrotizing fasciitis, other procedures were not collected. This omission may have led to either over‐estimation or under‐estimation of the impact of inappropriate therapy on the outcomes of interest.
The fact that our cohort represents a single large urban academic tertiary care medical center may limit the generalizability of our results only to centers that share similar characteristics. Finally, similar to most other studies of this type, ours lacks data on posthospitalization outcomes and for this reason limits itself to hospital outcomes only.
In summary, we have shown that, similar to other populations with HCAI, a substantial proportion (nearly 1/4) of cSSSI patients with HCAI receive inappropriate empiric therapy for their infection, and this early exposure, though not affecting hospital mortality, is associated with a significant prolongation of the hospitalization by as much as 2 days. Studies are needed to refine decision rules for risk‐stratifying patients with cSSSI HCAI in order to determine the probability of infection with a resistant organism. In turn, such instruments at the bedside may assure improved utilization of appropriately targeted empiric therapy that will both optimize individual patient outcomes and reduce the risk of emergence of antimicrobial resistance.
Appendix
| Principal diagnosis code | Description |
|---|---|
| 680 | Carbuncle and furuncle |
| 681 | Cellulitis and abscess of finger and toe |
| 682 | Other cellulitis and abscess |
| 683 | Acute lymphadenitis |
| 685 | Pilonidal cyst with abscess |
| 686 | Other local infections of skin and subcutaneous tissue |
| 707 | Decubitus ulcer |
| 707.1 | Ulcers of lower limbs, except decubitus |
| 707.8 | Chronic ulcer of other specified sites |
| 707.9 | Chronic ulcer of unspecified site |
| 958.3 | Posttraumatic wound infection, not elsewhere classified |
| 996.62 | Infection due to other vascular device, implant, and graft |
| 997.62 | Infection (chronic) of amputation stump |
| 998.5 | Postoperative wound infection |
| Diagnosis code | Description |
|---|---|
| 728.86 | Necrotizing fasciitis |
| 785.4 | Gangrene |
| 686.09 | Ecthyma gangrenosum |
| 730.00730.2 | Osteomyelitis |
| 630677 | Complications of pregnancy, childbirth and puerperium |
| 288.0 | Neutropenia |
| 684 | Impetigo |
| Procedure code | |
| 39.95 | Plasmapheresis |
| 99.71 | Hemoperfusion |
- ,,, et al.Invasive methicillin‐resistant Staphylococcus aureus infections in the United States.JAMA.2007;298:1762–1771.
- ,,, et al.Methicillin‐resistant S. aureus infections among patients in the emergency department.N Engl J Med.2006;17;355:666–674.
- Hospital‐Acquired Pneumonia Guideline Committee of the American Thoracic Society and Infectious Diseases Society of America.Guidelines for the management of adults with hospital‐acquired pneumonia, ventilator‐associated pneumonia, and healthcare‐associated pneumonia.Am J Respir Crit Care Med.2005;171:388–416.
- ,,, et al.Epidemiology and outcomes of health‐care‐associated pneumonia: Results from a large US database of culture‐positive pneumonia.Chest.2005;128:3854–3862.
- ,,, et al.Health care‐associated bloodstream infections in adults: A reason to change the accepted definition of community‐acquired infections.Ann Intern Med.2002;137:791–797.
- ,,,,,.Healthcare‐associated bloodstream infection: A distinct entity? Insights from a large U.S. database.Crit Care Med.2006;34:2588–2595.
- ,,, et al.Health care‐associated pneumonia and community‐acquired pneumonia: a single‐center experience.Antimicrob Agents Chemother.2007;51:3568–3573.
- ,,,,,.Clinical and economic consequences of failure of initial antibiotic therapy for hospitalized patients with complicated skin and skin‐structure infections.Infect Control Hosp Epidemiol.2008;29:160–169.
- ,,, et al.Skin, soft tissue, bone, and joint infections in hospitalized patients: Epidemiology and microbiological, clinical, and economic outcomes.Infect Control Hosp Epidemiol.2007;28:1290–1298.
- ,,, et al.Methicillin‐resistant Staphylococcus aureus sterile‐site infection: The importance of appropriate initial antimicrobial treatment.Crit Care Med.2006;34:2069–2074.
- ,,, et al.The influence of inadequate antimicrobial treatment of bloodstream infections on patient outcomes in the ICU setting.Chest.2000;118:146–155.
- ,.Modification of empiric antibiotic treatment in patients with pneumonia acquired in the intensive care unit.Intensive Care Med.1996;22:387–394.
- ,,, et al.Clinical importance of delays in the initiation of appropriate antibiotic treatment for ventilator‐associated pneumonia.Chest.2002;122:262–268.
- ,,,,.Antimicrobial therapy escalation and hospital mortality among patients with HCAP: A single center experience.Chest.2008;134:963–968.
- ,,, et al.Cost of gram‐negative resistance.Crit Care Med.2007;35:89–95.
- ,,, et al.Epidemiology and outcomes of hospitalizations with complicated skin and skin‐structure infections: implications of healthcare‐associated infection risk factors.Infect Control Hosp Epidemiol.2009;30:1203–1210.
- ,,.Inappropriate therapy for methicillin‐resistant Staphylococcus aureus: resource utilization and cost implications.Crit Care Med.2008;36:2335–2340.
Classically, infections have been categorized as either community‐acquired (CAI) or nosocomial in origin. Until recently, this scheme was thought adequate to capture the differences in the microbiology and outcomes in the corresponding scenarios. However, recent evidence suggests that this distinction may no longer be valid. For example, with the spread and diffusion of healthcare delivery beyond the confines of the hospital along with the increasing use of broad spectrum antibiotics both in and out of the hospital, pathogens such as methicillin‐resistant Staphylococcus aureus (MRSA) and Pseudomonas aeruginosa (PA), traditionally thought to be confined to the hospital, are now seen in patients presenting from the community to the emergency department (ED).1, 2 Reflecting this shift in epidemiology, some national guidelines now recognize healthcare‐associated infection (HCAI) as a distinct entity.3 The concept of HCAI allows the clinician to identify patients who, despite suffering a community onset infection, still may be at risk for a resistant bacterial pathogen. Recent studies in both bloodstream infection and pneumonia have clearly demonstrated that those with HCAI have distinct microbiology and outcomes relative to those with pure CAI.47
Most work focusing on establishing HCAI has not addressed skin and soft tissue infections. These infections, although not often fatal, account for an increasing number of admissions to the hospital.8, 9 In addition, they may be associated with substantial morbidity and cost.8 Given that many pathogens such as S. aureus, which may be resistant to typical antimicrobials used in the ED, are also major culprits in complicated skin and skin structure infections (cSSSI), the HCAI paradigm may apply in cSSSI. Furthermore, because of these patterns of increased resistance, HCA‐cSSSI patients, similar to other HCAI groups, may be at an increased risk of being treated with initially inappropriate antibiotic therapy.7, 10
Since in the setting of other types of infection inappropriate empiric treatment has been shown to be associated with increased mortality and costs,7, 1015 and since indirect evidence suggests a similar impact on healthcare utilization among cSSSI patients,8 we hypothesized that among a cohort of patients hospitalized with a cSSSI, the initial empiric choice of therapy is independently associated with hospital length of stay (LOS). We performed a retrospective cohort study to address this question.
Methods
Study Design
We performed a single‐center retrospective cohort study of patients with cSSSI admitted to the hospital through the ED. All consecutive patients hospitalized between April 2006 and December 2007 meeting predefined inclusion criteria (see below) were enrolled. The study was approved by the Washington University School of Medicine Human Studies Committee, and informed consent was waived. We have previously reported on the characteristics and outcomes of this cohort, including both community‐acquired and HCA‐cSSSI patients.16
Study Cohort
All consecutive patients admitted from the community through the ED between April 2006 and December 2007 at the Barnes‐Jewish Hospital, a 1200‐bed university‐affiliated, urban teaching hospital in St. Louis, MO were included if: (1) they had a diagnosis of a predefined cSSSI (see Appendix Table A1, based on reference 8) and (2) they had a positive microbiology culture obtained within 24 hours of hospital admission. Similar to the work by Edelsberg et al.8 we excluded patients if certain diagnoses and procedures were present (Appendix Table A2). Cases were also excluded if they represented a readmission for the same diagnosis within 30 days of the original hospitalization.
Definitions
HCAI was defined as any cSSSI in a patient with a history of recent hospitalization (within the previous year, consistent with the previous study16), receiving antibiotics prior to admission (previous 90 days), transferring from a nursing home, or needing chronic dialysis. We defined a polymicrobial infection as one with more than one organism, and mixed infection as an infection with both a gram‐positive and a gram‐negative organism. Inappropriate empiric therapy took place if a patient did not receive treatment within 24 hours of the time the culture was obtained with an agent exhibiting in vitro activity against the isolated pathogen(s). In mixed infections, appropriate therapy was treatment within 24 hours of culture being obtained with agent(s) active against all pathogens recovered.
Data Elements
We collected information about multiple baseline demographic and clinical factors including: age, gender, race/ethnicity, comorbidities, the presence of risk factors for HCAI, the presence of bacteremia at admission, and the location of admission (ward vs. intensive care unit [ICU]). Bacteriology data included information on specific bacterium/a recovered from culture, the site of the culture (eg, tissue, blood), susceptibility patterns, and whether the infection was monomicrobial, polymicrobial, or mixed. When blood culture was available and positive, we prioritized this over wound and other cultures and designated the corresponding organism as the culprit in the index infection. Cultures growing our coagulase‐negative S. aureus were excluded as a probable contaminant. Treatment data included information on the choice of the antimicrobial therapy and the timing of its institution relative to the timing of obtaining the culture specimen. The presence of such procedures as incision and drainage (I&D) or debridement was recorded.
Statistical Analyses
Descriptive statistics comparing HCAI patients treated appropriately to those receiving inappropriate empiric coverage based on their clinical, demographic, microbiologic and treatment characteristics were computed. Hospital LOS served as the primary and hospital mortality as the secondary outcomes, comparing patients with HCAI treated appropriately to those treated inappropriately. All continuous variables were compared using Student's t test or the Mann‐Whitney U test as appropriate. All categorical variables were compared using the chi‐square test or Fisher's exact test. To assess the attributable impact of inappropriate therapy in HCAI on the outcomes of interest, general linear models with log transformation were developed to model hospital LOS parameters; all means are presented as geometric means. All potential risk factors significant at the 0.1 level in univariate analyses were entered into the model. All calculations were performed in Stata version 9 (Statacorp, College Station, TX).
Results
Of the 717 patients with culture‐positive cSSSI admitted during the study period, 527 (73.5%) were classified as HCAI. The most common reason for classification as an HCAI was recent hospitalization. Among those with an HCA‐cSSSI, 405 (76.9%) received appropriate empiric treatment, with nearly one‐quarter receiving inappropriate initial coverage. Those receiving inappropriate antibiotic were more likely to be African American, and had a higher likelihood of having end‐stage renal disease (ESRD) than those with appropriate coverage (Table 1). While those patients treated appropriately had higher rates of both cellulitis and abscess as the presenting infection, a substantially higher proportion of those receiving inappropriate initial treatment had a decubitus ulcer (29.5% vs. 10.9%, P <0.001), a device‐associated infection (42.6% vs. 28.6%, P = 0.004), and had evidence of bacteremia (68.9% vs. 57.8%, P = 0.028) than those receiving appropriate empiric coverage (Table 2).
| Inappropriate (n = 122), n (%) | Appropriate (n = 405), n (%) | P Value | |
|---|---|---|---|
| |||
| Age, years | 56.3 18.0 | 53.6 16.7 | 0.147 |
| Gender (F) | 62 (50.8) | 190 (46.9) | 0.449 |
| Race | |||
| Caucasian | 51 (41.8) | 219 (54.1) | 0.048 |
| African American | 68 (55.7) | 178 (43.9) | |
| Other | 3 (2.5) | 8 (2.0) | |
| HCAI risk factors | |||
| Recent hospitalization* | 110 (90.2) | 373 (92.1) | 0.498 |
| Within 90 days | 98 (80.3) | 274 (67.7) | 0.007 |
| >90 and 180 days | 52 (42.6) | 170 (42.0) | 0.899 |
| >180 days and 1 year | 46 (37.7) | 164 (40.5) | 0.581 |
| Prior antibiotics | 26 (21.3) | 90 (22.2) | 0.831 |
| Nursing home resident | 29 (23.8) | 54 (13.3) | 0.006 |
| Hemodialysis | 19 (15.6) | 39 (9.7) | 0.067 |
| Comorbidities | |||
| DM | 40 (37.8) | 128 (31.6) | 0.806 |
| PVD | 5 (4.1) | 15 (3.7) | 0.841 |
| Liver disease | 6 (4.9) | 33 (8.2) | 0.232 |
| Cancer | 21 (17.2) | 85 (21.0) | 0.362 |
| HIV | 1 (0.8) | 12 (3.0) | 0.316 |
| Organ transplant | 2 (1.6) | 8 (2.0) | 1.000 |
| Autoimmune disease | 5 (4.1) | 8 (2.0) | 0.185 |
| ESRD | 22 (18.0) | 46 (11.4) | 0.054 |
| Inappropriate (n = 122), n (%) | Appropriate (n = 405), n (%) | P Value | |
|---|---|---|---|
| |||
| Cellulitis | 28 (23.0) | 171 (42.2) | <0.001 |
| Decubitus ulcer | 36 (29.5) | 44 (10.9) | <0.001 |
| Post‐op wound | 25 (20.5) | 75 (18.5) | 0.626 |
| Device‐associated infection | 52 (42.6) | 116 (28.6) | 0.004 |
| Diabetic foot ulcer | 9 (7.4) | 24 (5.9) | 0.562 |
| Abscess | 22 (18.0) | 108 (26.7) | 0.052 |
| Other* | 2 (1.6) | 17 (4.2) | 0.269 |
| Presence of bacteremia | 84 (68.9) | 234 (57.8) | 0.028 |
The pathogens recovered from the appropriately and inappropriately treated groups are listed in Figure 1. While S. aureus overall was more common among those treated appropriately, the frequency of MRSA did not differ between the groups. Both E. faecalis and E. faecium were recovered more frequently in the inappropriate group, resulting in a similar pattern among the vancomycin‐resistant enterococcal species. Likewise, P. aeruginosa, P. mirabilis, and A. baumannii were all more frequently seen in the group treated inappropriately than in the group getting appropriate empiric coverage. A mixed infection was also more likely to be present among those not exposed (16.5%) than among those exposed (7.5%) to appropriate early therapy (P = 0.001) (Figure 1).
In terms of processes of care and outcomes (Table 3), commensurate with the higher prevalence of abscess in the appropriately treated group, the rate of I&D was significantly higher in this cohort (36.8%) than in the inappropriately treated (23.0%) group (P = 0.005). Need for initial ICU care did not differ as a function of appropriateness of therapy (P = 0.635).
| Inappropriate (n = 122) | Appropriate (n = 405) | P Value | |
|---|---|---|---|
| |||
| I&D/debridement | 28 (23.0%) | 149 (36.8%) | 0.005 |
| I&D in ED | 0 | 7 (1.7) | 0.361 |
| ICU | 9 (7.4%) | 25 (6.2%) | 0.635 |
| Hospital LOS, days | |||
| Median (IQR 25, 75) [Range] | 7.0 (4.2, 13.6) [0.686.6] | 6 (3.3, 10.1) [0.748.3] | 0.026 |
| Hospital mortality | 9 (7.4%) | 26 (6.4%) | 0.710 |
The unadjusted mortality rate was low overall and did not vary based on initial treatment (Table 3). In a generalized linear model with the log‐transformed LOS as the dependent variable, adjusting for multiple potential confounders, initial inappropriate antibiotic therapy had an attributable incremental increase in the hospital LOS of 1.8 days (95% CI, 1.42.3) (Table 4).
| Factor | Attributable LOS (days) | 95% CI | P Value |
|---|---|---|---|
| |||
| Infection type: device | 3.6 | 2.74.8 | <0.001 |
| Infection type: decubitus ulcer | 3.3 | 2.64.2 | <0.001 |
| Infection type: abscess | 2.5 | 1.64.0 | <0.001 |
| Organism: P. mirabilis | 2.2 | 1.43.4 | <0.001 |
| Organism: E. faecalis | 2.1 | 1.72.6 | <0.001 |
| Nursing home resident | 2.1 | 1.62.6 | <0.001 |
| Inappropriate antibiotic | 1.8 | 1.42.3 | <0.001 |
| Race: Non‐Caucasian | 0.31 | 0.240.41 | <0.001 |
| Organism: E. faecium | 0.23 | 0.150.35 | <0.001 |
Because bacteremia is known to be an effect modifier of the relationship between the empiric choice of antibiotic and infection outcomes, we further explored its role in the HCAI cSSSI on the outcomes of interest (Table 5). Similar to the effect detected in the overall cohort, treatment with inappropriate therapy was associated with an increase in the hospital LOS, but not hospital mortality in those with bacteremia, though this phenomenon was observed only among patients with secondary bacteremia, and not among those without (Table 5).
| Bacteremia Present (n = 318) | Bacteremia Absent (n = 209) | |||||
|---|---|---|---|---|---|---|
| I (n = 84) | A (n = 234) | P Value | I (n = 38) | A (n = 171) | P Value | |
| ||||||
| Hospital LOS, days | ||||||
| Mean SD | 14.4 27.5 | 9.8 9.7 | 0.041 | 6.6 6.8 | 6.9 8.2 | 0.761 |
| Median (IQR 25, 75) | 8.8 (5.4, 13.9) | 7.0 (4.3, 11.7) | 4.4 (2.4, 7.7) | 3.9 (2.0, 8.2) | ||
| Hospital mortality | 8 (9.5%) | 24 (10.3%) | 0.848 | 1 (2.6%) | 2 (1.2%) | 0.454 |
Discussion
This retrospective analysis provides evidence that inappropriate empiric antibiotic therapy for HCA‐cSSSI independently prolongs hospital LOS. The impact of inappropriate initial treatment on LOS is independent of many important confounders. In addition, we observed that this effect, while present among patients with secondary bacteremia, is absent among those without a blood stream infection.
To the best of our knowledge, ours is the first cohort study to examine the outcomes associated with inappropriate treatment of a HCAI cSSSI within the context of available microbiology data. Edelsberg et al.8 examined clinical and economic outcomes associated with the failure of the initial treatment of cSSSI. While not specifically focusing on HCAI patients, these authors noted an overall 23% initial therapy failure rate. Among those patients who failed initial therapy, the risk of hospital death was nearly 3‐fold higher (adjusted odds ratio [OR], 2.91; 95% CI, 2.343.62), and they incurred the mean of 5.4 additional hospital days, compared to patients treated successfully with the initial regimen.8 Our study confirms Edelsberg et al.'s8 observation of prolonged hospital LOS in association with treatment failure, and builds upon it by defining the actual LOS increment attributable to inappropriate empiric therapy. It is worth noting that the study by Edelsberg et al.,8 however, lacked explicit definition of the HCAI population and microbiology data, and used treatment failure as a surrogate marker for inappropriate treatment. It is likely these differences between our two studies in the underlying population and exposure definitions that account for the differences in the mortality data between that study and ours.
It is not fundamentally surprising that early exposure to inappropriate empiric therapy alters healthcare resource utilization outcomes for the worse. Others have demonstrated that infection with a resistant organism results in prolongation of hospital LOS and costs. For example, in a large cohort of over 600 surgical hospitalizations requiring treatment for a gram‐negative infection, antibiotic resistance was an independent predictor of increased LOS and costs.15 These authors quantified the incremental burden of early gram‐negative resistance at over $11,000 in hospital costs.15 Unfortunately, the treatment differences for resistant and sensitive organisms were not examined.15 Similarly, Shorr et al. examined risk factors for prolonged hospital LOS and increased costs in a cohort of 291 patients with MRSA sterile site infection.17 Because in this study 23% of the patients received inappropriate empiric therapy, the authors were able to examine the impact of this exposure on utilization outcomes.17 In an adjusted analysis, inappropriate initial treatment was associated with an incremental increase in the LOS of 2.5 days, corresponding to the unadjusted cost differential of nearly $6,000.17 Although focusing on a different population, our results are consistent with these previous observations that antibiotic resistance and early inappropriate therapy affect hospital utilization parameters, in our case by adding nearly 2 days to the hospital LOS.
Our study has a number of limitations. First, as a retrospective cohort study it is prone to various forms of bias, most notably selection bias. To minimize the possibility of such, we established a priori case definitions and enrolled consecutive patients over a specific period of time. Second, as in any observational study, confounding is an issue. We dealt with this statistically by constructing a multivariable regression model; however, the possibility of residual confounding remains. Third, because some of the wound and ulcer cultures likely were obtained with a swab and thus represented colonization, rather than infection, we may have over‐estimated the rate of inappropriate therapy, and this needs to be followed up in future prospective studies. Similarly, we may have over‐estimated the likelihood of inappropriate therapy among polymicrobial and mixed infections as well, given that, for example, a gram‐negative organism may carry a different clinical significance when cultured from blood (infection) than when it is detected in a decubitus ulcer (potential colonization). Fourth, because we limited our cohort to patients without deep‐seated infections, such as necrotizing fasciitis, other procedures were not collected. This omission may have led to either over‐estimation or under‐estimation of the impact of inappropriate therapy on the outcomes of interest.
The fact that our cohort represents a single large urban academic tertiary care medical center may limit the generalizability of our results only to centers that share similar characteristics. Finally, similar to most other studies of this type, ours lacks data on posthospitalization outcomes and for this reason limits itself to hospital outcomes only.
In summary, we have shown that, similar to other populations with HCAI, a substantial proportion (nearly 1/4) of cSSSI patients with HCAI receive inappropriate empiric therapy for their infection, and this early exposure, though not affecting hospital mortality, is associated with a significant prolongation of the hospitalization by as much as 2 days. Studies are needed to refine decision rules for risk‐stratifying patients with cSSSI HCAI in order to determine the probability of infection with a resistant organism. In turn, such instruments at the bedside may assure improved utilization of appropriately targeted empiric therapy that will both optimize individual patient outcomes and reduce the risk of emergence of antimicrobial resistance.
Appendix
| Principal diagnosis code | Description |
|---|---|
| 680 | Carbuncle and furuncle |
| 681 | Cellulitis and abscess of finger and toe |
| 682 | Other cellulitis and abscess |
| 683 | Acute lymphadenitis |
| 685 | Pilonidal cyst with abscess |
| 686 | Other local infections of skin and subcutaneous tissue |
| 707 | Decubitus ulcer |
| 707.1 | Ulcers of lower limbs, except decubitus |
| 707.8 | Chronic ulcer of other specified sites |
| 707.9 | Chronic ulcer of unspecified site |
| 958.3 | Posttraumatic wound infection, not elsewhere classified |
| 996.62 | Infection due to other vascular device, implant, and graft |
| 997.62 | Infection (chronic) of amputation stump |
| 998.5 | Postoperative wound infection |
| Diagnosis code | Description |
|---|---|
| 728.86 | Necrotizing fasciitis |
| 785.4 | Gangrene |
| 686.09 | Ecthyma gangrenosum |
| 730.00730.2 | Osteomyelitis |
| 630677 | Complications of pregnancy, childbirth and puerperium |
| 288.0 | Neutropenia |
| 684 | Impetigo |
| Procedure code | |
| 39.95 | Plasmapheresis |
| 99.71 | Hemoperfusion |
Classically, infections have been categorized as either community‐acquired (CAI) or nosocomial in origin. Until recently, this scheme was thought adequate to capture the differences in the microbiology and outcomes in the corresponding scenarios. However, recent evidence suggests that this distinction may no longer be valid. For example, with the spread and diffusion of healthcare delivery beyond the confines of the hospital along with the increasing use of broad spectrum antibiotics both in and out of the hospital, pathogens such as methicillin‐resistant Staphylococcus aureus (MRSA) and Pseudomonas aeruginosa (PA), traditionally thought to be confined to the hospital, are now seen in patients presenting from the community to the emergency department (ED).1, 2 Reflecting this shift in epidemiology, some national guidelines now recognize healthcare‐associated infection (HCAI) as a distinct entity.3 The concept of HCAI allows the clinician to identify patients who, despite suffering a community onset infection, still may be at risk for a resistant bacterial pathogen. Recent studies in both bloodstream infection and pneumonia have clearly demonstrated that those with HCAI have distinct microbiology and outcomes relative to those with pure CAI.47
Most work focusing on establishing HCAI has not addressed skin and soft tissue infections. These infections, although not often fatal, account for an increasing number of admissions to the hospital.8, 9 In addition, they may be associated with substantial morbidity and cost.8 Given that many pathogens such as S. aureus, which may be resistant to typical antimicrobials used in the ED, are also major culprits in complicated skin and skin structure infections (cSSSI), the HCAI paradigm may apply in cSSSI. Furthermore, because of these patterns of increased resistance, HCA‐cSSSI patients, similar to other HCAI groups, may be at an increased risk of being treated with initially inappropriate antibiotic therapy.7, 10
Since in the setting of other types of infection inappropriate empiric treatment has been shown to be associated with increased mortality and costs,7, 1015 and since indirect evidence suggests a similar impact on healthcare utilization among cSSSI patients,8 we hypothesized that among a cohort of patients hospitalized with a cSSSI, the initial empiric choice of therapy is independently associated with hospital length of stay (LOS). We performed a retrospective cohort study to address this question.
Methods
Study Design
We performed a single‐center retrospective cohort study of patients with cSSSI admitted to the hospital through the ED. All consecutive patients hospitalized between April 2006 and December 2007 meeting predefined inclusion criteria (see below) were enrolled. The study was approved by the Washington University School of Medicine Human Studies Committee, and informed consent was waived. We have previously reported on the characteristics and outcomes of this cohort, including both community‐acquired and HCA‐cSSSI patients.16
Study Cohort
All consecutive patients admitted from the community through the ED between April 2006 and December 2007 at the Barnes‐Jewish Hospital, a 1200‐bed university‐affiliated, urban teaching hospital in St. Louis, MO were included if: (1) they had a diagnosis of a predefined cSSSI (see Appendix Table A1, based on reference 8) and (2) they had a positive microbiology culture obtained within 24 hours of hospital admission. Similar to the work by Edelsberg et al.8 we excluded patients if certain diagnoses and procedures were present (Appendix Table A2). Cases were also excluded if they represented a readmission for the same diagnosis within 30 days of the original hospitalization.
Definitions
HCAI was defined as any cSSSI in a patient with a history of recent hospitalization (within the previous year, consistent with the previous study16), receiving antibiotics prior to admission (previous 90 days), transferring from a nursing home, or needing chronic dialysis. We defined a polymicrobial infection as one with more than one organism, and mixed infection as an infection with both a gram‐positive and a gram‐negative organism. Inappropriate empiric therapy took place if a patient did not receive treatment within 24 hours of the time the culture was obtained with an agent exhibiting in vitro activity against the isolated pathogen(s). In mixed infections, appropriate therapy was treatment within 24 hours of culture being obtained with agent(s) active against all pathogens recovered.
Data Elements
We collected information about multiple baseline demographic and clinical factors including: age, gender, race/ethnicity, comorbidities, the presence of risk factors for HCAI, the presence of bacteremia at admission, and the location of admission (ward vs. intensive care unit [ICU]). Bacteriology data included information on specific bacterium/a recovered from culture, the site of the culture (eg, tissue, blood), susceptibility patterns, and whether the infection was monomicrobial, polymicrobial, or mixed. When blood culture was available and positive, we prioritized this over wound and other cultures and designated the corresponding organism as the culprit in the index infection. Cultures growing our coagulase‐negative S. aureus were excluded as a probable contaminant. Treatment data included information on the choice of the antimicrobial therapy and the timing of its institution relative to the timing of obtaining the culture specimen. The presence of such procedures as incision and drainage (I&D) or debridement was recorded.
Statistical Analyses
Descriptive statistics comparing HCAI patients treated appropriately to those receiving inappropriate empiric coverage based on their clinical, demographic, microbiologic and treatment characteristics were computed. Hospital LOS served as the primary and hospital mortality as the secondary outcomes, comparing patients with HCAI treated appropriately to those treated inappropriately. All continuous variables were compared using Student's t test or the Mann‐Whitney U test as appropriate. All categorical variables were compared using the chi‐square test or Fisher's exact test. To assess the attributable impact of inappropriate therapy in HCAI on the outcomes of interest, general linear models with log transformation were developed to model hospital LOS parameters; all means are presented as geometric means. All potential risk factors significant at the 0.1 level in univariate analyses were entered into the model. All calculations were performed in Stata version 9 (Statacorp, College Station, TX).
Results
Of the 717 patients with culture‐positive cSSSI admitted during the study period, 527 (73.5%) were classified as HCAI. The most common reason for classification as an HCAI was recent hospitalization. Among those with an HCA‐cSSSI, 405 (76.9%) received appropriate empiric treatment, with nearly one‐quarter receiving inappropriate initial coverage. Those receiving inappropriate antibiotic were more likely to be African American, and had a higher likelihood of having end‐stage renal disease (ESRD) than those with appropriate coverage (Table 1). While those patients treated appropriately had higher rates of both cellulitis and abscess as the presenting infection, a substantially higher proportion of those receiving inappropriate initial treatment had a decubitus ulcer (29.5% vs. 10.9%, P <0.001), a device‐associated infection (42.6% vs. 28.6%, P = 0.004), and had evidence of bacteremia (68.9% vs. 57.8%, P = 0.028) than those receiving appropriate empiric coverage (Table 2).
| Inappropriate (n = 122), n (%) | Appropriate (n = 405), n (%) | P Value | |
|---|---|---|---|
| |||
| Age, years | 56.3 18.0 | 53.6 16.7 | 0.147 |
| Gender (F) | 62 (50.8) | 190 (46.9) | 0.449 |
| Race | |||
| Caucasian | 51 (41.8) | 219 (54.1) | 0.048 |
| African American | 68 (55.7) | 178 (43.9) | |
| Other | 3 (2.5) | 8 (2.0) | |
| HCAI risk factors | |||
| Recent hospitalization* | 110 (90.2) | 373 (92.1) | 0.498 |
| Within 90 days | 98 (80.3) | 274 (67.7) | 0.007 |
| >90 and 180 days | 52 (42.6) | 170 (42.0) | 0.899 |
| >180 days and 1 year | 46 (37.7) | 164 (40.5) | 0.581 |
| Prior antibiotics | 26 (21.3) | 90 (22.2) | 0.831 |
| Nursing home resident | 29 (23.8) | 54 (13.3) | 0.006 |
| Hemodialysis | 19 (15.6) | 39 (9.7) | 0.067 |
| Comorbidities | |||
| DM | 40 (37.8) | 128 (31.6) | 0.806 |
| PVD | 5 (4.1) | 15 (3.7) | 0.841 |
| Liver disease | 6 (4.9) | 33 (8.2) | 0.232 |
| Cancer | 21 (17.2) | 85 (21.0) | 0.362 |
| HIV | 1 (0.8) | 12 (3.0) | 0.316 |
| Organ transplant | 2 (1.6) | 8 (2.0) | 1.000 |
| Autoimmune disease | 5 (4.1) | 8 (2.0) | 0.185 |
| ESRD | 22 (18.0) | 46 (11.4) | 0.054 |
| Inappropriate (n = 122), n (%) | Appropriate (n = 405), n (%) | P Value | |
|---|---|---|---|
| |||
| Cellulitis | 28 (23.0) | 171 (42.2) | <0.001 |
| Decubitus ulcer | 36 (29.5) | 44 (10.9) | <0.001 |
| Post‐op wound | 25 (20.5) | 75 (18.5) | 0.626 |
| Device‐associated infection | 52 (42.6) | 116 (28.6) | 0.004 |
| Diabetic foot ulcer | 9 (7.4) | 24 (5.9) | 0.562 |
| Abscess | 22 (18.0) | 108 (26.7) | 0.052 |
| Other* | 2 (1.6) | 17 (4.2) | 0.269 |
| Presence of bacteremia | 84 (68.9) | 234 (57.8) | 0.028 |
The pathogens recovered from the appropriately and inappropriately treated groups are listed in Figure 1. While S. aureus overall was more common among those treated appropriately, the frequency of MRSA did not differ between the groups. Both E. faecalis and E. faecium were recovered more frequently in the inappropriate group, resulting in a similar pattern among the vancomycin‐resistant enterococcal species. Likewise, P. aeruginosa, P. mirabilis, and A. baumannii were all more frequently seen in the group treated inappropriately than in the group getting appropriate empiric coverage. A mixed infection was also more likely to be present among those not exposed (16.5%) than among those exposed (7.5%) to appropriate early therapy (P = 0.001) (Figure 1).
In terms of processes of care and outcomes (Table 3), commensurate with the higher prevalence of abscess in the appropriately treated group, the rate of I&D was significantly higher in this cohort (36.8%) than in the inappropriately treated (23.0%) group (P = 0.005). Need for initial ICU care did not differ as a function of appropriateness of therapy (P = 0.635).
| Inappropriate (n = 122) | Appropriate (n = 405) | P Value | |
|---|---|---|---|
| |||
| I&D/debridement | 28 (23.0%) | 149 (36.8%) | 0.005 |
| I&D in ED | 0 | 7 (1.7) | 0.361 |
| ICU | 9 (7.4%) | 25 (6.2%) | 0.635 |
| Hospital LOS, days | |||
| Median (IQR 25, 75) [Range] | 7.0 (4.2, 13.6) [0.686.6] | 6 (3.3, 10.1) [0.748.3] | 0.026 |
| Hospital mortality | 9 (7.4%) | 26 (6.4%) | 0.710 |
The unadjusted mortality rate was low overall and did not vary based on initial treatment (Table 3). In a generalized linear model with the log‐transformed LOS as the dependent variable, adjusting for multiple potential confounders, initial inappropriate antibiotic therapy had an attributable incremental increase in the hospital LOS of 1.8 days (95% CI, 1.42.3) (Table 4).
| Factor | Attributable LOS (days) | 95% CI | P Value |
|---|---|---|---|
| |||
| Infection type: device | 3.6 | 2.74.8 | <0.001 |
| Infection type: decubitus ulcer | 3.3 | 2.64.2 | <0.001 |
| Infection type: abscess | 2.5 | 1.64.0 | <0.001 |
| Organism: P. mirabilis | 2.2 | 1.43.4 | <0.001 |
| Organism: E. faecalis | 2.1 | 1.72.6 | <0.001 |
| Nursing home resident | 2.1 | 1.62.6 | <0.001 |
| Inappropriate antibiotic | 1.8 | 1.42.3 | <0.001 |
| Race: Non‐Caucasian | 0.31 | 0.240.41 | <0.001 |
| Organism: E. faecium | 0.23 | 0.150.35 | <0.001 |
Because bacteremia is known to be an effect modifier of the relationship between the empiric choice of antibiotic and infection outcomes, we further explored its role in the HCAI cSSSI on the outcomes of interest (Table 5). Similar to the effect detected in the overall cohort, treatment with inappropriate therapy was associated with an increase in the hospital LOS, but not hospital mortality in those with bacteremia, though this phenomenon was observed only among patients with secondary bacteremia, and not among those without (Table 5).
| Bacteremia Present (n = 318) | Bacteremia Absent (n = 209) | |||||
|---|---|---|---|---|---|---|
| I (n = 84) | A (n = 234) | P Value | I (n = 38) | A (n = 171) | P Value | |
| ||||||
| Hospital LOS, days | ||||||
| Mean SD | 14.4 27.5 | 9.8 9.7 | 0.041 | 6.6 6.8 | 6.9 8.2 | 0.761 |
| Median (IQR 25, 75) | 8.8 (5.4, 13.9) | 7.0 (4.3, 11.7) | 4.4 (2.4, 7.7) | 3.9 (2.0, 8.2) | ||
| Hospital mortality | 8 (9.5%) | 24 (10.3%) | 0.848 | 1 (2.6%) | 2 (1.2%) | 0.454 |
Discussion
This retrospective analysis provides evidence that inappropriate empiric antibiotic therapy for HCA‐cSSSI independently prolongs hospital LOS. The impact of inappropriate initial treatment on LOS is independent of many important confounders. In addition, we observed that this effect, while present among patients with secondary bacteremia, is absent among those without a blood stream infection.
To the best of our knowledge, ours is the first cohort study to examine the outcomes associated with inappropriate treatment of a HCAI cSSSI within the context of available microbiology data. Edelsberg et al.8 examined clinical and economic outcomes associated with the failure of the initial treatment of cSSSI. While not specifically focusing on HCAI patients, these authors noted an overall 23% initial therapy failure rate. Among those patients who failed initial therapy, the risk of hospital death was nearly 3‐fold higher (adjusted odds ratio [OR], 2.91; 95% CI, 2.343.62), and they incurred the mean of 5.4 additional hospital days, compared to patients treated successfully with the initial regimen.8 Our study confirms Edelsberg et al.'s8 observation of prolonged hospital LOS in association with treatment failure, and builds upon it by defining the actual LOS increment attributable to inappropriate empiric therapy. It is worth noting that the study by Edelsberg et al.,8 however, lacked explicit definition of the HCAI population and microbiology data, and used treatment failure as a surrogate marker for inappropriate treatment. It is likely these differences between our two studies in the underlying population and exposure definitions that account for the differences in the mortality data between that study and ours.
It is not fundamentally surprising that early exposure to inappropriate empiric therapy alters healthcare resource utilization outcomes for the worse. Others have demonstrated that infection with a resistant organism results in prolongation of hospital LOS and costs. For example, in a large cohort of over 600 surgical hospitalizations requiring treatment for a gram‐negative infection, antibiotic resistance was an independent predictor of increased LOS and costs.15 These authors quantified the incremental burden of early gram‐negative resistance at over $11,000 in hospital costs.15 Unfortunately, the treatment differences for resistant and sensitive organisms were not examined.15 Similarly, Shorr et al. examined risk factors for prolonged hospital LOS and increased costs in a cohort of 291 patients with MRSA sterile site infection.17 Because in this study 23% of the patients received inappropriate empiric therapy, the authors were able to examine the impact of this exposure on utilization outcomes.17 In an adjusted analysis, inappropriate initial treatment was associated with an incremental increase in the LOS of 2.5 days, corresponding to the unadjusted cost differential of nearly $6,000.17 Although focusing on a different population, our results are consistent with these previous observations that antibiotic resistance and early inappropriate therapy affect hospital utilization parameters, in our case by adding nearly 2 days to the hospital LOS.
Our study has a number of limitations. First, as a retrospective cohort study it is prone to various forms of bias, most notably selection bias. To minimize the possibility of such, we established a priori case definitions and enrolled consecutive patients over a specific period of time. Second, as in any observational study, confounding is an issue. We dealt with this statistically by constructing a multivariable regression model; however, the possibility of residual confounding remains. Third, because some of the wound and ulcer cultures likely were obtained with a swab and thus represented colonization, rather than infection, we may have over‐estimated the rate of inappropriate therapy, and this needs to be followed up in future prospective studies. Similarly, we may have over‐estimated the likelihood of inappropriate therapy among polymicrobial and mixed infections as well, given that, for example, a gram‐negative organism may carry a different clinical significance when cultured from blood (infection) than when it is detected in a decubitus ulcer (potential colonization). Fourth, because we limited our cohort to patients without deep‐seated infections, such as necrotizing fasciitis, other procedures were not collected. This omission may have led to either over‐estimation or under‐estimation of the impact of inappropriate therapy on the outcomes of interest.
The fact that our cohort represents a single large urban academic tertiary care medical center may limit the generalizability of our results only to centers that share similar characteristics. Finally, similar to most other studies of this type, ours lacks data on posthospitalization outcomes and for this reason limits itself to hospital outcomes only.
In summary, we have shown that, similar to other populations with HCAI, a substantial proportion (nearly 1/4) of cSSSI patients with HCAI receive inappropriate empiric therapy for their infection, and this early exposure, though not affecting hospital mortality, is associated with a significant prolongation of the hospitalization by as much as 2 days. Studies are needed to refine decision rules for risk‐stratifying patients with cSSSI HCAI in order to determine the probability of infection with a resistant organism. In turn, such instruments at the bedside may assure improved utilization of appropriately targeted empiric therapy that will both optimize individual patient outcomes and reduce the risk of emergence of antimicrobial resistance.
Appendix
| Principal diagnosis code | Description |
|---|---|
| 680 | Carbuncle and furuncle |
| 681 | Cellulitis and abscess of finger and toe |
| 682 | Other cellulitis and abscess |
| 683 | Acute lymphadenitis |
| 685 | Pilonidal cyst with abscess |
| 686 | Other local infections of skin and subcutaneous tissue |
| 707 | Decubitus ulcer |
| 707.1 | Ulcers of lower limbs, except decubitus |
| 707.8 | Chronic ulcer of other specified sites |
| 707.9 | Chronic ulcer of unspecified site |
| 958.3 | Posttraumatic wound infection, not elsewhere classified |
| 996.62 | Infection due to other vascular device, implant, and graft |
| 997.62 | Infection (chronic) of amputation stump |
| 998.5 | Postoperative wound infection |
| Diagnosis code | Description |
|---|---|
| 728.86 | Necrotizing fasciitis |
| 785.4 | Gangrene |
| 686.09 | Ecthyma gangrenosum |
| 730.00730.2 | Osteomyelitis |
| 630677 | Complications of pregnancy, childbirth and puerperium |
| 288.0 | Neutropenia |
| 684 | Impetigo |
| Procedure code | |
| 39.95 | Plasmapheresis |
| 99.71 | Hemoperfusion |
- ,,, et al.Invasive methicillin‐resistant Staphylococcus aureus infections in the United States.JAMA.2007;298:1762–1771.
- ,,, et al.Methicillin‐resistant S. aureus infections among patients in the emergency department.N Engl J Med.2006;17;355:666–674.
- Hospital‐Acquired Pneumonia Guideline Committee of the American Thoracic Society and Infectious Diseases Society of America.Guidelines for the management of adults with hospital‐acquired pneumonia, ventilator‐associated pneumonia, and healthcare‐associated pneumonia.Am J Respir Crit Care Med.2005;171:388–416.
- ,,, et al.Epidemiology and outcomes of health‐care‐associated pneumonia: Results from a large US database of culture‐positive pneumonia.Chest.2005;128:3854–3862.
- ,,, et al.Health care‐associated bloodstream infections in adults: A reason to change the accepted definition of community‐acquired infections.Ann Intern Med.2002;137:791–797.
- ,,,,,.Healthcare‐associated bloodstream infection: A distinct entity? Insights from a large U.S. database.Crit Care Med.2006;34:2588–2595.
- ,,, et al.Health care‐associated pneumonia and community‐acquired pneumonia: a single‐center experience.Antimicrob Agents Chemother.2007;51:3568–3573.
- ,,,,,.Clinical and economic consequences of failure of initial antibiotic therapy for hospitalized patients with complicated skin and skin‐structure infections.Infect Control Hosp Epidemiol.2008;29:160–169.
- ,,, et al.Skin, soft tissue, bone, and joint infections in hospitalized patients: Epidemiology and microbiological, clinical, and economic outcomes.Infect Control Hosp Epidemiol.2007;28:1290–1298.
- ,,, et al.Methicillin‐resistant Staphylococcus aureus sterile‐site infection: The importance of appropriate initial antimicrobial treatment.Crit Care Med.2006;34:2069–2074.
- ,,, et al.The influence of inadequate antimicrobial treatment of bloodstream infections on patient outcomes in the ICU setting.Chest.2000;118:146–155.
- ,.Modification of empiric antibiotic treatment in patients with pneumonia acquired in the intensive care unit.Intensive Care Med.1996;22:387–394.
- ,,, et al.Clinical importance of delays in the initiation of appropriate antibiotic treatment for ventilator‐associated pneumonia.Chest.2002;122:262–268.
- ,,,,.Antimicrobial therapy escalation and hospital mortality among patients with HCAP: A single center experience.Chest.2008;134:963–968.
- ,,, et al.Cost of gram‐negative resistance.Crit Care Med.2007;35:89–95.
- ,,, et al.Epidemiology and outcomes of hospitalizations with complicated skin and skin‐structure infections: implications of healthcare‐associated infection risk factors.Infect Control Hosp Epidemiol.2009;30:1203–1210.
- ,,.Inappropriate therapy for methicillin‐resistant Staphylococcus aureus: resource utilization and cost implications.Crit Care Med.2008;36:2335–2340.
- ,,, et al.Invasive methicillin‐resistant Staphylococcus aureus infections in the United States.JAMA.2007;298:1762–1771.
- ,,, et al.Methicillin‐resistant S. aureus infections among patients in the emergency department.N Engl J Med.2006;17;355:666–674.
- Hospital‐Acquired Pneumonia Guideline Committee of the American Thoracic Society and Infectious Diseases Society of America.Guidelines for the management of adults with hospital‐acquired pneumonia, ventilator‐associated pneumonia, and healthcare‐associated pneumonia.Am J Respir Crit Care Med.2005;171:388–416.
- ,,, et al.Epidemiology and outcomes of health‐care‐associated pneumonia: Results from a large US database of culture‐positive pneumonia.Chest.2005;128:3854–3862.
- ,,, et al.Health care‐associated bloodstream infections in adults: A reason to change the accepted definition of community‐acquired infections.Ann Intern Med.2002;137:791–797.
- ,,,,,.Healthcare‐associated bloodstream infection: A distinct entity? Insights from a large U.S. database.Crit Care Med.2006;34:2588–2595.
- ,,, et al.Health care‐associated pneumonia and community‐acquired pneumonia: a single‐center experience.Antimicrob Agents Chemother.2007;51:3568–3573.
- ,,,,,.Clinical and economic consequences of failure of initial antibiotic therapy for hospitalized patients with complicated skin and skin‐structure infections.Infect Control Hosp Epidemiol.2008;29:160–169.
- ,,, et al.Skin, soft tissue, bone, and joint infections in hospitalized patients: Epidemiology and microbiological, clinical, and economic outcomes.Infect Control Hosp Epidemiol.2007;28:1290–1298.
- ,,, et al.Methicillin‐resistant Staphylococcus aureus sterile‐site infection: The importance of appropriate initial antimicrobial treatment.Crit Care Med.2006;34:2069–2074.
- ,,, et al.The influence of inadequate antimicrobial treatment of bloodstream infections on patient outcomes in the ICU setting.Chest.2000;118:146–155.
- ,.Modification of empiric antibiotic treatment in patients with pneumonia acquired in the intensive care unit.Intensive Care Med.1996;22:387–394.
- ,,, et al.Clinical importance of delays in the initiation of appropriate antibiotic treatment for ventilator‐associated pneumonia.Chest.2002;122:262–268.
- ,,,,.Antimicrobial therapy escalation and hospital mortality among patients with HCAP: A single center experience.Chest.2008;134:963–968.
- ,,, et al.Cost of gram‐negative resistance.Crit Care Med.2007;35:89–95.
- ,,, et al.Epidemiology and outcomes of hospitalizations with complicated skin and skin‐structure infections: implications of healthcare‐associated infection risk factors.Infect Control Hosp Epidemiol.2009;30:1203–1210.
- ,,.Inappropriate therapy for methicillin‐resistant Staphylococcus aureus: resource utilization and cost implications.Crit Care Med.2008;36:2335–2340.
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